The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
Solving Enterprise Data Challenges with Apache ArrowWes McKinney
This document discusses Apache Arrow, an open-source library that enables fast and efficient data interchange and processing. It summarizes the growth of Arrow and its ecosystem, including new features like the Arrow C++ query engine and Arrow Rust DataFusion. It also highlights how enterprises are using Arrow to solve challenges around data interoperability, access speed, query performance, and embeddable analytics. Case studies describe how companies like Microsoft, Google Cloud, Snowflake, and Meta leverage Arrow in their products and platforms. The presenter promotes Voltron Data's enterprise subscription and upcoming conference to support business use of Apache Arrow.
The document compares the query execution plans produced by Apache Hive and PostgreSQL. It shows that Hive's old-style execution plans are overly verbose and difficult to understand, providing many low-level details across multiple stages. In contrast, PostgreSQL's plans are more concise and readable, showing the logical query plan in a top-down manner with actual table names and fewer lines of text. The document advocates for Hive to adopt a simpler execution plan format similar to PostgreSQL's.
The columnar roadmap: Apache Parquet and Apache ArrowJulien Le Dem
This document discusses Apache Parquet and Apache Arrow, open source projects for columnar data formats. Parquet is an on-disk columnar format that optimizes I/O performance through compression and projection pushdown. Arrow is an in-memory columnar format that maximizes CPU efficiency through vectorized processing and SIMD. It aims to serve as a standard in-memory format between systems. The document outlines how Arrow builds on Parquet's success and provides benefits like reduced serialization overhead and ability to share functionality through its ecosystem. It also describes how Parquet and Arrow representations are integrated through techniques like vectorized reading and predicate pushdown.
1) Columnar formats like Parquet, Kudu and Arrow provide more efficient data storage and querying by organizing data by column rather than row.
2) Parquet provides an immutable columnar format well-suited for storage, while Kudu allows for mutable updates but is optimized for scans. Arrow provides an in-memory columnar format focused on CPU efficiency.
3) By establishing common in-memory and on-disk columnar standards, Arrow and Parquet enable more efficient data sharing and querying across systems without serialization overhead.
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
Kudu is a storage engine for Hadoop designed to address gaps in Hadoop's ability to handle workloads that require both high-throughput data ingestion and low-latency random access. It is a columnar storage engine that uses a log-structured merge tree to store data and provides APIs for NoSQL and SQL access. Kudu aims to provide high performance for both scans and random access through its columnar design and tablet architecture that partitions data across servers.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
This document summarizes a benchmark study of file formats for Hadoop, including Avro, JSON, ORC, and Parquet. It found that ORC with zlib compression generally performed best for full table scans. However, Avro with Snappy compression worked better for datasets with many shared strings. The document recommends experimenting with the benchmarks, as performance can vary based on data characteristics and use cases like column projections.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
Apache Hive 3 introduces new capabilities for data analytics including materialized views, default columns, constraints, and improved JDBC and Kafka connectors to enable real-time streaming and integration with external systems like Druid; Hive 3 also improves performance and query optimization through a new query result cache, workload management, and cloud storage optimizations. Data Analytics Studio provides self-service analytics on top of Hive 3 through a visual interface to optimize queries, monitor performance, and manage data lifecycles.
Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy
The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and ...Databricks
Uber has real needs to provide faster, fresher data to data consumers & products, running hundreds of thousands of analytical queries everyday. Uber engineers will share the design, architecture & use-cases of the second generation of ‘Hudi’, a self contained Apache Spark library to build large scale analytical datasets designed to serve such needs and beyond. Hudi (formerly Hoodie) is created to effectively manage petabytes of analytical data on distributed storage, while supporting fast ingestion & queries. In this talk, we will discuss how we leveraged Spark as a general purpose distributed execution engine to build Hudi, detailing tradeoffs & operational experience. We will also show to ingest data into Hudi using Spark Datasource/Streaming APIs and build Notebooks/Dashboards on top using Spark SQL.
"Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code."
Cost-based Query Optimization in Apache Phoenix using Apache CalciteJulian Hyde
This document summarizes a presentation on using Apache Calcite for cost-based query optimization in Apache Phoenix. Key points include:
- Phoenix is adding Calcite's query planning capabilities to improve performance and SQL compliance over its existing query optimizer.
- Calcite models queries as relational algebra expressions and uses rules, statistics, and a cost model to choose the most efficient execution plan.
- Examples show how Calcite rules like filter pushdown and exploiting sortedness can generate better plans than Phoenix's existing optimizer.
- Materialized views and interoperability with other Calcite data sources like Apache Drill are areas for future improvement beyond the initial Phoenix+Calcite integration.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Designing Structured Streaming Pipelines—How to Architect Things RightDatabricks
"Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark's built-in functions make it easy for developers to express complex computations. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem needs to be solved.
What are you trying to consume? Single source? Joining multiple streaming sources? Joining streaming with static data?
What are you trying to produce? What is the final output that the business wants? What type of queries does the business want to run on the final output?
When do you want it? When does the business want to the data? What is the acceptable latency? Do you really want to millisecond-level latency?
How much are you willing to pay for it? This is the ultimate question and the answer significantly determines how feasible is it solve the above questions.
These are the questions that we ask every customer in order to help them design their pipeline. In this talk, I am going to go through the decision tree of designing the right architecture for solving your problem."
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Kudu is an open source storage layer developed by Cloudera that provides low latency queries on large datasets. It uses a columnar storage format for fast scans and an embedded B-tree index for fast random access. Kudu tables are partitioned into tablets that are distributed and replicated across a cluster. The Raft consensus algorithm ensures consistency during replication. Kudu is suitable for applications requiring real-time analytics on streaming data and time-series queries across large datasets.
Spark Summit EU talk by Berni SchieferSpark Summit
This document summarizes experiences using the TPC-DS benchmark with Spark SQL 2.0 and 2.1 on a large cluster designed for Spark. It describes the configuration of the "F1" cluster including its hardware, operating system, Spark, and network settings. Initial results show that Spark SQL 2.0 provides significant improvements over earlier versions. While most queries completed successfully, some queries failed or ran very slowly, indicating areas for further optimization.
This document discusses Netflix's use of Spark on Yarn for ETL workloads. Some key points:
- Netflix runs Spark on Yarn across 3000 EC2 nodes to process large amounts of streaming data from over 100 million daily users.
- Technical challenges included optimizing performance for S3, dynamic resource allocation, and Parquet read/write. Improvements led to up to 18x faster job completion times.
- Production Spark applications include recommender systems that analyze user behavior and personalize content across billions of profiles and titles.
Zhan Zhang presents improvements made to bring HBase data efficiently into Spark with DataFrame support. The improvements include high performance by moving computation to data and reducing network overhead through partition pruning and column pruning. Full DataFrame support is provided, allowing Spark SQL and integrated language queries to run on existing HBase tables with Java primitive type support.
Kudu is an open source storage engine that provides low-latency random reads and writes while also supporting efficient analytical queries. It horizontally partitions and replicates data across servers for high availability and performance. Kudu integrates with Hadoop ecosystems tools like Impala, Spark, and MapReduce. The demo will cover Kudu architecture, data storage, and how to implement Kudu in a buffer load using Scala and Impala.
Spark Summit EU talk by Erwin Datema and Roeland van HamSpark Summit
The document discusses KeyGene's use of Apache Spark for high-throughput genomics data analysis. KeyGene is a crop innovation company that analyzes genomic data from thousands of plants to improve crop traits like yield and quality. They previously used conventional HPC clusters for genomics pipelines but found Spark enabled more interactive analysis. KeyGene developed a "Sparkified" genomics pipeline using tools like BWA, GATK and their own Guacamole variant caller. This allowed interactive variant selection and GWAS using Spark SQL queries, demonstrating Spark is well-suited for interactive plant genomics analysis.
Following the classical software architecture patterns we tend to design large monolith of software applications.
These monoliths are typically quite difficult to scale as they often require powerful machines, making the option to scale out very expensive.
In most cases these monoliths of software are designed to run on a single machine only, hence scaling out is complicated or even impossible without refactoring large portions of the application.
Therefore a new design pattern called microservices arose.
The pattern of microservices keeps the need of a clustered server setup in mind and helps to keep the application very modular.
This allows to simplify a scale out of your application and even allows to scale the bottlenecks of your application only and hence reducing the total cost for a scale out approach.
In this talk I will introduce the concept of microservices, how they are defined and how to design an application with them.
Furthermore I will show how to scale the application properly and why this is only possible due to the use of microservices.
Also we will have a look at Node.js and why it is a perfect, though not the only, fit to this design strategy.
However scaling is not the only purpose of microservices, they also increase the flexibility and maintainability of applications, this will also be discussed in the talk.
The document summarizes new features in Oracle Database 12c from Oracle 11g that would help a DBA currently using 11g. It lists and briefly describes features such as the READ privilege, temporary undo, online data file move, DDL logging, and many others. The objectives are to make the DBA aware of useful 12c features when working with a 12c database and to discuss each feature at a high level within 90 seconds.
Oracle Database 12c includes over 500 new features. Some key new features include:
- Oracle Database 12c Express (EM Express) which replaces Database Control and has less features than Database Control but does not require Java or an app server.
- New online capabilities like online DDL operations with no DDL locking, online move of partitions with no impact to queries, and online statistics gathering for bulk loads.
- Adaptive SQL Plan Management which allows the optimizer to select a more optimal plan at execution time based on current statistics.
- Multitenant architecture which allows consolidation of multiple databases into one container database with pluggable databases.
Oracle12 - The Top12 Features by NAYA TechnologiesNAYATech
The document discusses the top 12 new features of Oracle 12c, as presented by David Yahalom of NAYA Technologies. It covers improved column defaults, increased size limits, improved top-N queries, temporary UNDO, new partitioning features, transaction guard, adaptive execution plans, enhanced statistics, data optimization and information lifecycle management (ILM), row pattern matching, and a 50% discount code for a Oracle performance tuning seminar offered by NAYA Technologies.
This document discusses containers and virtual machines. It explains that containers provide a lightweight virtualization method that isolates applications but shares the host operating system kernel. Containers use resource isolation features like cgroups and namespaces to limit CPU, memory, storage, and networking usage. In contrast, virtual machines run their own full operating system and provide stronger isolation but are more resource intensive.
This document introduces Spark SQL 1.3.0 and how to optimize efficiency. It discusses the main objects like SQL Context and how to create DataFrames from RDDs, JSON, and perform operations like select, filter, groupBy, join, and save data. It shows how to register DataFrames as tables and write SQL queries. DataFrames also support RDD actions and transformations. The document provides references for learning more about DataFrames and their development direction.
You've seen the basic 2-stage example Spark Programs, and now you're ready to move on to something larger. I'll go over lessons I've learned for writing efficient Spark programs, from design patterns to debugging tips.
The slides are largely just talking points for a live presentation, but hopefully you can still make sense of them for offline viewing as well.
The document discusses various benchmarks that are commonly used to evaluate Semantic Web repositories and their performance handling large amounts of RDF data. Some of the major benchmarks mentioned include the Lehigh University Benchmark (LUBM), Berlin SPARQL Benchmark (BSBM), SP2Bench, Social Network Intelligence Benchmark (SIB), and DBPedia SPARQL Benchmark. The document also provides an overview of different benchmark components and links to resources with performance results from various RDF stores and systems.
Data Science at Scale: Using Apache Spark for Data Science at BitlySarah Guido
Given at Data Day Seattle 2015.
Bitly generates over 9 billion clicks on shortened links a month, as well as over 100 million unique link shortens. Analyzing data of this scale is not without its challenges. At Bitly, we have started adopting Apache Spark as a way to process our data. In this talk, I’ll elaborate on how I use Spark as part of my data science workflow. I’ll cover how Spark fits into our existing architecture, the kind of problems I’m solving with Spark, and the benefits and challenges of using Spark for large-scale data science.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
The document outlines an agenda for a workshop on Pandas, data wrangling, and data science using Pandas. The agenda includes: an introduction and setup; discussing the data science pipeline and Pandas APIs/namespaces; basic Pandas maneuvers; data wrangling techniques like transformations, aggregations, and joins; hands-on exercises using datasets like Titanic and RecSys-2015; and a Q&A session. The goals are to understand data wrangling with Pandas through interactive examples and hands-on practice with real datasets.
The document outlines an agenda for a conference on Apache Spark and data science, including sessions on Spark's capabilities and direction, using DataFrames in PySpark, linear regression, text analysis, classification, clustering, and recommendation engines using Spark MLlib. Breakout sessions are scheduled between many of the technical sessions to allow for hands-on work and discussion.
Apache Kudu (Incubating): New Hadoop Storage for Fast Analytics on Fast Data ...Cloudera, Inc.
This document provides an overview of Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Some key points:
- Kudu is a columnar storage engine that allows for both fast analytics queries as well as low-latency updates to the stored data.
- It addresses gaps in the existing Hadoop storage landscape by providing efficient scans, individual row lookups, and mutable data all within the same system.
- Kudu uses a master-tablet server architecture with tablets that are horizontally partitioned and replicated for fault tolerance. It supports SQL and NoSQL interfaces.
- Integrations with Spark, Impala and MapReduce allow it to be used for both
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
Kudu: New Hadoop Storage for Fast Analytics on Fast DataCloudera, Inc.
The document discusses Kudu, an open source storage system for Hadoop that is designed to enable both transactional and analytic workloads. Kudu uses a columnar storage format and provides ACID transactions for fast analytics on fast data. It aims to address gaps in Hadoop for workloads that require simultaneous random access and scanning of data. Benchmarks show Kudu can perform TPC-H queries within 2x of Parquet storage, with low latency for reads and writes on solid state drives.
Kudu is a storage layer developed by Cloudera that is designed for fast analytics on fast data. It aims to provide high throughput for large scans and low latency for individual reads and writes. Kudu tables can be queried using SQL and provide database-like semantics like ACID transactions. Kudu is optimized for workloads that require both sequential and random read/write access patterns, such as time series data, machine data analytics, and online reporting. It provides improvements over traditional Hadoop storage systems by eliminating complex ETL pipelines and enabling immediate access to new data.
February 2016 HUG: Apache Kudu (incubating): New Apache Hadoop Storage for Fa...Yahoo Developer Network
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Apache Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets. Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads. This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem with out-of-the-box integration with Apache Spark, that fills the gap described above to provide a new option to achieve fast scans and fast random access from a single API.
Speakers:
David Alves. Software engineer at Cloudera working on the Kudu team, and a PhD student at UT Austin. David is a committer at the Apache Software Foundation and has contributed to several open source projects, including Apache Cassandra and Apache Drill.
Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...Data Con LA
1) Apache Kudu is a new updatable columnar storage engine for Apache Hadoop that facilitates fast analytics on fast data.
2) Kudu is designed to address gaps in the current Hadoop storage landscape by providing both high throughput for big scans and low latency for short accesses simultaneously.
3) Kudu integrates with various Hadoop components like Spark, Impala, MapReduce to enable SQL queries and other analytics workloads on fast updating data.
This document discusses Kudu, an open source storage system for Hadoop that provides fast analytics on fast data. It was built by Cloudera to address gaps in Hadoop's storage technologies by providing low-latency transactions and fast scans. The document outlines Kudu's design goals, architecture using columnar storage and Raft consensus, performance benchmarks showing faster analytics than Parquet and HBase, and two use cases at Chinese company Xiaomi where Kudu improved their analytics pipelines.
Cloudera Impala - Las Vegas Big Data Meetup Nov 5th 2014cdmaxime
Maxime Dumas gives a presentation on Cloudera Impala, which provides fast SQL query capability for Apache Hadoop. Impala allows for interactive queries on Hadoop data in seconds rather than minutes by using a native MPP query engine instead of MapReduce. It offers benefits like SQL support, improved performance of 3-4x up to 90x faster than MapReduce, and flexibility to query existing Hadoop data without needing to migrate or duplicate it. The latest release of Impala 2.0 includes new features like window functions, subqueries, and spilling joins and aggregations to disk when memory is exhausted.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
Introducing Apache Kudu (Incubating) - Montreal HUG May 2016Mladen Kovacevic
The document introduces Apache Kudu (incubating), a new updatable columnar storage system for Apache Hadoop designed for fast analytics on fast and changing data. It was designed to simplify architectures that use HDFS and HBase together. Kudu aims to provide high throughput for scans, low latency for individual rows, and database-like ACID transactions. It uses a columnar format and is optimized for SSD and new storage technologies.
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld
VMworld 2013
Michael Corey, Ntirety, Inc
Jeff Szastak, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
The document discusses Kudu, a new updatable columnar storage system for Hadoop that was built to address gaps in transactional and analytic capabilities of existing Hadoop storage technologies like HDFS and HBase. Kudu aims to provide both high throughput for large scans like HDFS and low latency for individual row lookups and updates like HBase, while supporting SQL queries and a relational data model. It leverages improvements in hardware by using a columnar format and indexes to improve CPU efficiency for these workloads compared to traditional storage systems. The document outlines Kudu's goals and capabilities and provides examples of use cases like time series analytics, machine data analytics and online reporting that would benefit from Kudu's simultaneous support for sequential
Bay Area Impala User Group Meetup (Sept 16 2014)Cloudera, Inc.
The document discusses Impala releases and roadmaps. It outlines key features released in different Impala versions, including SQL capabilities, performance improvements, and support for additional file formats and data types. It also describes Impala's performance advantages compared to other SQL-on-Hadoop systems and how its approach is expected to increasingly favor performance gains. Lastly, it encourages trying out Impala and engaging with their community.
Introducing Kudu, Big Data Warehousing MeetupCaserta
Not just an SQL interface or file system, Kudu - the new, updating column store for Hadoop, is changing the storage landscape. It's easy to operate and makes new data immediately available for analytics or operations.
At the Caserta Concepts Big Data Warehousing Meetup, our guests from Cloudera outlined the functionality of Kudu and talked about why it will become an integral component in big data warehousing on Hadoop.
To learn more about what Caserta Concepts has to offer, visit http://casertaconcepts.com/
This document summarizes the key points from a presentation on SQL Server 2016. It discusses in-memory and columnstore features, including performance gains from processing data in memory instead of on disk. New capabilities for real-time operational analytics are presented that allow analytics queries to run concurrently with OLTP workloads using the same data schema. Maintaining a columnstore index for analytics queries is suggested to improve performance.
Artur Borycki - Beyond Lambda - how to get from logical to physical - code.ta...AboutYouGmbH
Teradata believes in principles of self-service, automation, and on-demand resource allocation to enable faster, more efficient, and more effective data application development and operation. The document discusses the Lambda architecture, alternatives like the Kappa architecture, and a vision for an "Omega" architecture. It provides examples of how to build real-time data applications using microservices, event streaming, and loosely coupled services across Teradata and other data platforms like Hadoop.
Unlock Hadoop Success with Cloudera Navigator OptimizerCloudera, Inc.
Cloudera Navigator Optimizer analyzes existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop.
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...Cloudera, Inc.
3 Things to Learn About:
*How Apache Kudu enables users to do more than ever before with their Analytic and Operational Databases
*How Cloudera has built two versatile databases to help our customers tackle their hardest problems.
*How the addition of Apache Kudu to this mix will enable new use cases around real-time analytics, internet of things, time series data, and more.
Similar to Spark Summit EU talk by Mike Percy (20)
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit
In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...Spark Summit
In this talk, we’ll present techniques for visualizing large scale machine learning systems in Spark. These are techniques that are employed by Netflix to understand and refine the machine learning models behind Netflix’s famous recommender systems that are used to personalize the Netflix experience for their 99 millions members around the world. Essential to these techniques is Vegas, a new OSS Scala library that aims to be the “missing MatPlotLib” for Spark/Scala. We’ll talk about the design of Vegas and its usage in Scala notebooks to visualize Machine Learning Models.
This presentation introduces how we design and implement a real-time processing platform using latest Spark Structured Streaming framework to intelligently transform the production lines in the manufacturing industry. In the traditional production line there are a variety of isolated structured, semi-structured and unstructured data, such as sensor data, machine screen output, log output, database records etc. There are two main data scenarios: 1) Picture and video data with low frequency but a large amount; 2) Continuous data with high frequency. They are not a large amount of data per unit. However the total amount of them is very large, such as vibration data used to detect the quality of the equipment. These data have the characteristics of streaming data: real-time, volatile, burst, disorder and infinity. Making effective real-time decisions to retrieve values from these data is critical to smart manufacturing. The latest Spark Structured Streaming framework greatly lowers the bar for building highly scalable and fault-tolerant streaming applications. Thanks to the Spark we are able to build a low-latency, high-throughput and reliable operation system involving data acquisition, transmission, analysis and storage. The actual user case proved that the system meets the needs of real-time decision-making. The system greatly enhance the production process of predictive fault repair and production line material tracking efficiency, and can reduce about half of the labor force for the production lines.
Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...Spark Summit
Graph is on the rise and it’s time to start learning about scalable graph analytics! In this session we will go over two Spark-based Graph Analytics frameworks: Tinkerpop and GraphFrames. While both frameworks can express very similar traversals, they have different performance characteristics and APIs. In this Deep-Dive by example presentation, we will demonstrate some common traversals and explain how, at a Spark level, each traversal is actually computed under the hood! Learn both the fluent Gremlin API as well as the powerful GraphFrame Motif api as we show examples of both simultaneously. No need to be familiar with Graphs or Spark for this presentation as we’ll be explaining everything from the ground up!
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...Spark Summit
Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...Spark Summit
With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.
Next CERN Accelerator Logging Service with Jakub WozniakSpark Summit
The Next Accelerator Logging Service (NXCALS) is a new Big Data project at CERN aiming to replace the existing Oracle-based service.
The main purpose of the system is to store and present Controls/Infrastructure related data gathered from thousands of devices in the whole accelerator complex.
The data is used to operate the machines, improve their performance and conduct studies for new beam types or future experiments.
During this talk, Jakub will speak about NXCALS requirements and design choices that lead to the selected architecture based on Hadoop and Spark. He will present the Ingestion API, the abstractions behind the Meta-data Service and the Spark-based Extraction API where simple changes to the schema handling greatly improved the overall usability of the system. The system itself is not CERN specific and can be of interest to other companies or institutes confronted with similar Big Data problems.
Powering a Startup with Apache Spark with Kevin KimSpark Summit
In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.
Improving Traffic Prediction Using Weather Datawith Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...Spark Summit
In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects.
In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API.
For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.
How Nielsen Utilized Databricks for Large-Scale Research and Development with...Spark Summit
Nielsen used Databricks to test new digital advertising rating methodologies on a large scale. Databricks allowed Nielsen to run analyses on thousands of advertising campaigns using both small panel data and large production data. This identified edge cases and performance gains faster than traditional methods. Using Databricks reduced the time required to test and deploy improved rating methodologies to benefit Nielsen's clients.
Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...Spark Summit
Data lineage tracking is one of the significant problems that financial institutions face when using modern big data tools. This presentation describes Spline – a data lineage tracking and visualization tool for Apache Spark. Spline captures and stores lineage information from internal Spark execution plans and visualizes it in a user-friendly manner.
Goal Based Data Production with Sim SimeonovSpark Summit
Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.
Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...Spark Summit
Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.
Getting Ready to Use Redis with Apache Spark with Dvir VolkSpark Summit
Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...Spark Summit
Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...Spark Summit
The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.
Harnessing Wild and Untamed (Publicly Available) Data for the Cost efficient ...weiwchu
We recently discovered that models trained with large-scale speech datasets sourced from the web could achieve superior accuracy and potentially lower cost than traditionally human-labeled or simulated speech datasets. We developed a customizable AI-driven data labeling system. It infers word-level transcriptions with confidence scores, enabling supervised ASR training. It also robustly generates phone-level timestamps even in the presence of transcription or recognition errors, facilitating the training of TTS models. Moreover, It automatically assigns labels such as scenario, accent, language, and topic tags to the data, enabling the selection of task-specific data for training a model tailored to that particular task. We assessed the effectiveness of the datasets by fine-tuning open-source large speech models such as Whisper and SeamlessM4T and analyzing the resulting metrics. In addition to openly-available data, our data handling system can also be tailored to provide reliable labels for proprietary data from certain vertical domains. This customization enables supervised training of domain-specific models without the need for human labelers, eliminating data breach risks and significantly reducing data labeling cost.
Data analytics is a powerful tool that can transform business decision-making across industries. Contact District 11 Solutions, which specializes in data analytics, to make informed decisions and achieve your business goals.
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Getting Started with Interactive Brokers API and Python.pdfRiya Sen
In the fast-paced world of finance, automation is key to staying ahead of the curve. Traders and investors are increasingly turning to programming languages like Python to streamline their strategies and enhance their decision-making processes. In this blog post, we will delve into the integration of Python with Interactive Brokers, one of the leading brokerage platforms, and explore how this dynamic duo can revolutionize your trading experience.
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
DESIGN AND DEVELOPMENT OF AUTO OXYGEN CONCENTRATOR WITH SOS ALERT FOR HIKING ...JeevanKp7
Long-term oxygen therapy (LTOT) and novel techniques of evaluating treatment efficacy have enhanced the quality of life and decreased healthcare expenses for COPD patients.
The cost of a pulmonary blood gas test is comparable to the cost of two days of oxygen therapy and the cost of a hospital stay is equivalent to the cost of one month of oxygen therapy, long-term oxygen therapy (LTOT) is a cost-effective technique of treating this disease.
A small number of clinical investigations on LTOT have shown that it improves the quality of life of COPD patients by reducing the loss of their respiratory capacity. A study of 8487 Danish patients found that LTOT for 1524 hours per day extended life expectancy from 1.07 to 1.40 years.
Annex K RBF's The World Game pdf documentSteven McGee
Signals & Telemetry Annex K for RBF's The World Game / Trade Federations / USPTO 13/573,002 Heart Beacon Cycle Time - Space Time Chain meters, metrics, standards. Adaptive Procedural template framework structured data derived from DoD / NATO's system of systems engineering tech framework
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...rightmanforbloodline
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.