This document discusses Pinterest's data architecture and use of Pinball for workflow management. Pinterest processes 3 petabytes of data daily from their 60 billion pins and 1 billion boards across a 2000 node Hadoop cluster. They use Kafka, Secor and Singer for ingesting event data. Pinball is used for workflow management to handle their scale of hundreds of workflows, thousands of jobs and 500+ jobs in some workflows. Pinball provides simple abstractions, extensibility, reliability, debuggability and horizontal scalability for workflow execution.
This document provides an introduction to Amazon Aurora, AWS's managed relational database service. It discusses how Aurora was built to provide the speed and availability of commercial databases at the simplicity and cost-effectiveness of open source databases. The document outlines key Aurora features like automatic scaling, continuous backups, replication across Availability Zones, and integration with other AWS services. Customer case studies show how Aurora provides better performance at lower costs than alternative database options. The document also covers migration options and how Aurora offers a simpler, more cost-effective database solution than on-premises or self-managed options.
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.
Spark's Role in the Big Data Ecosystem (Spark Summit 2014)Databricks
This document summarizes the growth and development of the Spark project. It notes that Spark has grown significantly over the past year in terms of contributors, companies involved, and lines of code. Spark is now one of the most active projects within the Apache Hadoop ecosystem. The document outlines major new additions to Spark including Spark SQL for structured data, MLlib for machine learning algorithms, and Java 8 APIs. It discusses the vision for Spark as a unified platform and standard library for big data applications.
Considerations for Data Access in the LakehouseDatabricks
Organizations are increasingly exploring lakehouse architectures with Databricks to combine the best of data lakes and data warehouses. Databricks SQL Analytics introduces new innovation on the “house” to deliver data warehousing performance with the flexibility of data lakes. The lakehouse supports a diverse set of use cases and workloads that require distinct considerations for data access. On the lake side, tables with sensitive data require fine-grained access control that are enforced across the raw data and derivative data products via feature engineering or transformations. Whereas on the house side, tables can require fine-grained data access such as row level segmentation for data sharing, and additional transformations using analytics engineering tools. On the consumption side, there are additional considerations for managing access from popular BI tools such as Tableau, Power BI or Looker.
The product team at Immuta, a Databricks partner, will share their experience building data access governance solutions for lakehouse architectures across different data lake and warehouse platforms to show how to set up data access for common scenarios for Databricks teams new to SQL Analytics.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
Alkin Tezuysal discusses his first 90 days working at ChistaDATA Inc. as EVP of Global Services. He has experience working with databases like MySQL, Oracle, and ClickHouse. ChistaDATA focuses on providing ClickHouse infrastructure operations through managed services, support, and consulting. ClickHouse is an open source columnar database that uses a shared-nothing architecture for high performance analytics workloads.
High-speed Database Throughput Using Apache Arrow Flight SQLScyllaDB
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
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.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
How We Optimize Spark SQL Jobs With parallel and sync IODatabricks
Although NVMe has been more and more popular these years, a large amount of HDD are still widely used in super-large scale big data clusters. In a EB-level data platform, IO(including decompression and decode) cost contributes a large proportion of Spark jobs’ cost. In another word, IO operation is worth optimizing.
In ByteDancen, we do a series of IO optimization to improve performance, including parallel read and asynchronized shuffle. Firstly we implement file level parallel read to improve performance when there are a lot of small files. Secondly, we design row group level parallel read to accelerate queries for big-file scenario. Thirdly, implement asynchronized spill to improve job peformance. Besides, we design parquet column family, which will split a table into a few column families and different column family will be in different Parquets files. Different column family can be read in parallel, so the read performance is much higher than the existing approach. In our practice, the end to end performance is improved by 5% to 30%
In this talk, I will illustrate how we implement these features and how they accelerate Apache Spark jobs.
Using Delta Lake to Transform a Legacy Apache Spark to Support Complex Update...Databricks
The convergence of big data technology towards traditional database domain has became an industry trend. At present, open source big data processing engines, such as Apache Spark, Apache Hadoop, Apache Flink, etc., already support SQL interfaces, and the usage of SQL basically occupies a dominant position. Companies use above open source software to build their own ETL framework and OLAP technology. However, in terms of OLTP technology, it is still a strong point of traditional databases. One of the main reasons is the support of ACID by traditional databases.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Python tools to deploy your machine learning models fasterJeff Hale
Comparing Gradio, Streamlit, and FastAPI (with a little discussion of flask)
Jeff Hale's presentation for Data Science DC March 8, 2022
Repository with code at https://github.com/discdiver/dsdc-deploy-models
EMR Spark tuning involves configuring Spark and YARN parameters like executor memory and cores to optimize performance. The default Spark configurations depend on the deployment method (Thrift, Zeppelin etc). YARN is used for resource management in cluster mode, and allocates resources to containers based on minimum and maximum thresholds. When tuning, factors like available cluster resources, executor instances and cores should be considered to avoid overcommitting resources.
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
More and more data sources today provide a constant data stream, from Internet of Things devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store them in a data sink of choice, such as a data lake implemented with HDFS/object store, or in a database such as a NoSQL or even an RDBMS, if the volume of events is not too high. Storing a high-volume event stream is feasible and not such a challenge anymore. But doing it adds to the end-to-end latency and it’s a matter of minutes or hours until you can present some results of your analytics. If you need to react fast, you simply can't afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
50 Billion pins and counting: Using Hadoop to build data driven ProductsDataWorks Summit
Pinterest uses Hadoop and tools they developed on top of it like Pinball and Pinalytics to harness data from their 50 billion pins and 1 billion boards. Pinball is Pinterest's workflow manager that provides simple abstractions and scales horizontally to process their 3 petabytes of data daily across thousands of jobs. Pinalytics is Pinterest's scalable data analytics engine that allows flexible querying and visualization of metrics data stored in HBase.
Pinterest uses Hadoop and tools they developed on top of it like Pinball and Pinalytics to harness data from their 50 billion pins and 1 billion boards. Pinball is Pinterest's workflow manager that provides simple abstractions and scales horizontally to process their 3 petabytes of data daily across thousands of jobs. Pinalytics is Pinterest's scalable data analytics engine that allows flexible querying and visualization of metrics data stored in HBase.
This document summarizes DreamObjects, an object storage platform powered by Ceph. It discusses the hardware used in storage and support nodes, including Intel and AMD processors, RAM, disks, and networking components. The document also provides details on Ceph configuration including replication, CRUSH mapping, OSD configuration, and application tuning. Monitoring tools discussed include Chef, pdsh, Sensu, collectd, graphite, logstash, Jenkins and future plans.
Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events, amounting to 3 PB, flowing through the Keystone infrastructure to help improve customer experience and glean business insights. The self-serve Keystone stream processing service processes these messages in near real-time with at-least once semantics in the cloud. This enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share the details about this platform, and our experience building it.
Openstack is open source software that allows users to create an Infrastructure as a Service (IaaS) cloud by pooling physical compute, storage, and network resources. It provides on-demand, scalable computing and storage through components like Nova (compute), Swift (object storage), Glance (images), Keystone (identity), and Quantum (networking). The presentation covers the architecture and components of Openstack, how it works from a user perspective, its history and motivation, partners, open development model, and the Openstack community in India.
Serverless SQL provides a serverless analytics platform that allows users to analyze data stored in object storage without having to manage infrastructure. Key features include seamless elasticity, pay-per-query consumption, and the ability to analyze data directly in object storage without having to move it. The platform includes serverless storage, data ingest, data transformation, analytics, and automation capabilities. It aims to create a sharing economy for analytics by allowing various users like developers, data engineers, and analysts flexible access to data and analytics.
Navigating SAP’s Integration Options (Mastering SAP Technologies 2013)Sascha Wenninger
Provides an overview of popular integration approaches, maps them to SAP's integration tools and concludes with some lessons learnt in their application.
Michael stack -the state of apache h basehdhappy001
The document provides an overview of Apache HBase, an open source, distributed, scalable, big data non-relational database. It discusses that HBase is modeled after Google's Bigtable and built on Hadoop for storage. It also summarizes that HBase is used by many large companies for applications such as messaging, real-time analytics, and search indexing. The project is led by an active community of committers and sees steady improvements and new features with each monthly release.
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Presto is an interactive SQL query engine for big data that was originally developed at Facebook in 2012 and open sourced in 2013. It is 10x faster than Hive for interactive queries on large datasets. Presto is highly extensible, supports pluggable backends, ANSI SQL, and complex queries. It uses an in-memory parallel processing architecture with pipelined task execution, data locality, caching, JIT compilation, and SQL optimizations to achieve high performance on large datasets.
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
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.
Low Latency Polyglot Model Scoring using Apache ApexApache Apex
This document discusses challenges in building low-latency machine learning applications and how Apache Apex can help address them. It introduces Apache Apex as a distributed streaming engine and describes how it allows embedding models from frameworks like R, Python, H2O through custom operators. It provides various data and model scoring patterns in Apex like dynamic resource allocation, checkpointing, exactly-once processing to meet SLAs. The document also demonstrates techniques like canary deployment, dormant models, model ensembles through logical overlays on the Apex DAG.
Sql Start! 2020 - SQL Server Lift & Shift su AzureMarco Obinu
Slide of the session delivered during SQL Start! 2020, where I illustrate different approaches to determine the best landing zone for you SQL Server workloads.
Video (ITA): https://youtu.be/1hqT_xHs0Qs
Low latency high throughput streaming using Apache Apex and Apache KuduDataWorks Summit
True streaming is fast becoming a necessity for many business use cases. On the other hand the data set sizes and volumes are also growing exponentially compounding the complexity of data processing pipelines.There exists a need for true low latency streaming coupled with very high throughput data processing. Apache Apex as a low latency and high throughput data processing framework and Apache Kudu as a high throughput store form a nice combination which solves this pattern very efficiently.
This session will walk through a use case which involves writing a high throughput stream using Apache Kafka,Apache Apex and Apache Kudu. The session will start with a general overview of Apache Apex and capabilities of Apex that form the foundation for a low latency and high throughput engine with Apache kafka being an example input source of streams. Subsequently we walk through Kudu integration with Apex by walking through various patterns like end to end exactly once, selective column writes and timestamp propagations for out of band data. The session will also cover additional patterns that this integration will cover for enterprise level data processing pipelines.
The session will conclude with some metrics for latency and throughput numbers for the use case that is presented.
Speaker
Ananth Gundabattula, Senior Architect, Commonwealth Bank of Australia
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
YARN: a resource manager for analytic platformTsuyoshi OZAWA
The document discusses YARN, a resource manager for Apache Hadoop. It provides an overview of YARN and its key features: (1) managing resources in a cluster, (2) managing application history logs, and (3) a service registry mechanism. It then discusses how distributed processing frameworks like Tez and Spark work on YARN, focusing on their directed acyclic graph (DAG) models and techniques for improving performance on YARN like container reuse.
The document summarizes announcements from AWS re:Invent 2016 related to compute, storage, artificial intelligence, serverless computing, databases, migration tools, and developer tools. Key announcements included new EC2 instance types, cost reductions, Elastic GPUs, AWS Batch for batch processing, Aurora PostgreSQL, Athena for analytics on S3 data, VMware on AWS, AWS X-Ray for tracing distributed applications, and expanded machine learning capabilities through services like Polly, Lex, and Rekognition as well as support for MXNet as an AI framework.
MyDBOPS Team has presented on Oracle MySQL user Camp ( 29-07-2016 ). This presentation is about Grafana and Prometheus for MySQL alerting and Dashboard setup.
Data Warehouse Modernization - Big Data in the Cloud Success with Qubole on O...Qubole
The effective use of big data is the key to gaining a competitive advantage and outperforming the competition. This change demands that companies consume and blend enormous amount of data created from divergent and inherently mismatched sources, which represents a paradigm shift to the traditional data warehouse.
Companies need to modernize their data warehouse, augmenting it with a platform that allows storage, processing, exploration and analysis of large and diverse datasets without limiting the ability to deliver the data access, and flexibility responding to the needs of the business. That’s where Oracle Cloud and Qubole work together delivering a new breed of data platform —capable of storing and processing the overwhelming amount of data that on-premises big data deployments cannot handle.
Watch this on-demand webinar to understand:
- Why deploying big data on-premises is expensive, complex to maintain and limits your ability to scale across new use cases and data sources
- How Oracle Bare Metal Cloud's predictable and fast performance compute and network services deliver the foundation of a cost-effective, high-performance big data platform
- How Qubole leverages Oracle Bare Metal Cloud to provide a turnkey big data service that optimizes cost, performance, and scale, enabling self-service data exploration.
Qubole delivers a cloud-based, turnkey, self-service big data service that removes the complexity and reduces the cost of doing big data. It leverages Oracle Bare Metal Cloud’s next generation of scalable, inexpensive and performant compute, network and storage public cloud infrastructure to provide a solution that accelerates time to market and reduces the risk of your big data initiatives.
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
A recent survey indicated significant growth of big data adoption among enterprise companies. The survey also indicated growing interest in Hadoop in the cloud.
Large companies see an opportunity to replace expensive legacy data warehouse applications with Big Data technologies. But how realistic is the notion of switching from tried and true data warehouse implementations to something that's still maturing, and what are the pitfalls? What will a business user need to learn in order to adapt to the new platform?
Spark on Yarn allows for dynamic provisioning of resources by allowing the Spark application master to request additional executors from Yarn as needed and release idle executors. This helps optimize resource utilization in the Yarn cluster. Qubole provides interfaces like the command UI, REST APIs, and SDKs to easily submit Spark jobs to Yarn clusters managed in Qubole, and integrates Spark with Hive by configuring Spark programs to access the Hive metastore. Key challenges include ensuring low overhead from Yarn, handling cached data, and network performance between clusters and shared services.
This document discusses running Spark on the cloud, including the advantages, challenges, and how Qubole addresses them. Some key advantages include using S3 for storage which allows independent scaling of storage and compute, ability to create ephemeral clusters on demand, and autoscaling capabilities. Challenges involve cluster lifecycle management, different interfaces needed, Spark autoscaling, debuggability across clusters, and handling spot instances. Qubole provides tools that automate cluster management, enable autoscaling of Spark, and make experiences seamless across clusters and interfaces.
Qubole is a cloud data analytics company founded in 2011 by former Facebook engineers. It provides a platform for interactive analytics on large datasets using Apache Spark and Presto on AWS. Qubole handles cluster management and scaling to enable self-service analytics without requiring Hadoop expertise. Customers span industries like advertising, healthcare, and retail and use Qubole for log analysis, machine learning, and business intelligence.
Qubole presentation for the Cleveland Big Data and Hadoop Meetup Qubole
Qubole provides a data platform as a service that allows companies to easily run analytics on large datasets without having to manage their own Hadoop clusters. The founders previously built Facebook's data platform. Qubole customers include Pinterest, an advertising company, and a healthcare company that leverages genomic data. Qubole's services provide scalability, ease of use, and unified metadata and governance for customers with varying analytics needs.
Qubole provides data infrastructure as a service, allowing companies to query big data on the cloud. It manages over an exabyte of data for companies and has made data use more agile. The service is used by developers, analysts, and business users at some companies. It processes large amounts of data and clusters resources on demand to provide flexibility and reduce costs compared to building infrastructure in-house.
Optimizing Big Data to run in the Public CloudQubole
Qubole is a cloud-based platform that allows customers to easily run Hadoop and Spark clusters on AWS for big data analytics. It optimizes performance and reduces costs through techniques like caching data in S3 for faster access, using spot instances, and directly writing query outputs to S3. The document discusses Qubole's features, capabilities, and how it provides an easier way for more users like data scientists and analysts to access and query big data compared to building and managing Hadoop clusters themselves.
Getting to 1.5M Ads/sec: How DataXu manages Big DataQubole
DataXu sits at the heart of the all-digital world, providing a data platform that manages tens of millions of dollars of digital advertising investments from Global 500 brands. The DataXu data platform evaluates 1.5 million online ad opportunities every second for our customers, allowing them to manage and optimize their marketing investments across all digital channels. DataXu employs a wide range of AWS services: Cloud Front, Cloud Trail, CloudWatch, Data Pipeline, Direct Connect, Dynamo DB, EC2, EMR, Glacier, IAM, Kinesis, RDS, Redshift, Route53, S3, SNS, SQS, and VPC to run various workloads at scale for DataXu data platform.
In addition, DataXu also uses Qubole Data Service, QDS, to offer a Unified Analytics Interface tool to DataXu customers. Qubole, a member of APN provides self-managing Big data infrastructure in the Cloud which leverages spot pricing for cost-efficiencies, provides fast performance, and most importantly a streamlined user-interface for ease of use.
Attendees will learn how Qubole provided self-managing Hadoop clusters in the AWS Cloud accelerated DataXu’s batch-oriented analysis jobs; and how Qubole integration with Amazon Redshift enabled DataXu to preform low latency and interactive analysis. Further, in the session we'll take a look at how DataXu opened up QDS access to their customers using QDS user interface thereby providing them with a single tool for both batch-oriented and interactive analysis. By using the QDS user interface buyers of the DataXu data service could perform all manner of analysis against the data stored in their AWS S3 bucket.
Speakers:
Scott Ward
Solutions Architect at Amazon Web Services
Ashish Dubey
Solutions Architect at Qubole
Yekesa Kosuru
VP Engineering at DataXu
Whether you are interested in healthcare data analytics or looking to get started with big data and marketing, these fundamental principles from data experts will contribute to your success. http://www.qubole.com/new-series-big-data-tips/
This document discusses Hive and Presto for big data analytics in the cloud. It provides an overview of how big data has evolved from traditional analytics on internal data to using new external data sources at larger scales. It describes how the public cloud has changed the economics and flexibility of big data projects by providing cheap storage, elastic compute, and open-source big data software like Hadoop, Hive, and Presto. It compares Hive, which uses Hadoop MapReduce for execution, to Presto, which uses an in-memory pipelined execution model, and shows how Presto can provide faster performance for interactive queries.
Qubole offers Presto as a service, providing an interactive query engine that is 2.5-7x faster than Hive for querying data stored in S3. Customers can write queries without managing the Presto cluster, which Qubole handles along with scheduling, collaboration tools, and REST API support. Qubole has customized Presto for better integration with its Hadoop and Hive implementations, through optimizations, bug fixes, and pre-installed SerDes.
This webinar discusses how to perform sentiment analysis on large datasets using Apache Hive. It provides an overview of sentiment analysis and demonstrates useful Hive UDFs for preprocessing text data and extracting n-grams. The webinar also includes a tutorial analyzing sentiment around the topic of "mortgage" using the MemeTracker dataset containing 90 million records of URLs, timestamps, memes and links over 36GB of JSON data. Advanced custom sentiment analysis can be developed by extending Hive's extensibility framework.
A session from Qubole Best Practice Webinar Series- “Big Data Secrets from the Pros”. Covers how to make Apache Hive queries run faster by
a. Better layout of data on HDFS via partitioning and bucketing
b. Designing test queries by using block and bucket sampling before running the queries on large datasets
c. Using bucket map joins and parallel processing to run queries faster
Visit www.qubole.com for more information.
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.
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.
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.
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
Big Data and Analytics Shaping the future of PaymentsRuchiRathor2
The payments industry is experiencing a data-driven revolution powered by big data and analytics.
Here's a glimpse into 5 ways this dynamic duo is transforming how we pay.
In essence, big data and analytics are playing a pivotal role in building a future filled with faster, more secure, and convenient payment methods for everyone.
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
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.
4. Data at Pinterest
• 60 Billion Pins
• 1 Billion boards
• 100M MAU
• 60 PB of data on S3
• 3 PB processed every day
• 2000 node Hadoop cluster
• 250 engineers
15. • API for simplified
executor abstraction
• Advanced support
for spot instances
• Baked AMI
customization
Why Qubole?
• Hadoop & Spark as
managed services
• Tight integration with
Hive
• Graceful cluster
scaling
17. Confidentia
l
● Scale:
o 60 Billion Pins
o Hundreds of workflows
o Thousands of jobs
o 500+ jobs in a workflow
o 3 petabytes processed daily
● Support:
o Hadoop, Cascading, Hive, Spark …
Scale of Processing
job
workflow
18. Confidentia
l
Why Pinball?
● Requirements
o Simple abstractions
o Extensible in future
o Reliable stateless computing
o Easy to debug
o Scales horizontally
o Can be upgraded w/o aborting workflows
o Rich features like auto-retries, per-job emails, overrun
policies…
● Options
o Apache Oozie, Azkaban, Luigi
20. Confidentia
l
● Workflow
o A directed graph of
nodes called jobs
● Edge
o Run after
dependence
● Node
o Job is a node
Workflow Model
21. Confidentia
l
Job State
● Job state is captured in a token
● Tokens are named hierarchically
Master
Job Token
version: 123
name: /workflow/w1/job
owner: worker_0
expiration: 1234567
data: JobTemplate(....)
23. Confidentia
l
● Master keeps the state
● Workers claim and execute tasks
● Horizontally scalable
Master Worker Interaction
Worker Master Persistent Store
1: request 2: update
3: ack
24. Confidentia
l
Master
● Entire state is kept in memory
● Each state update is synchronously persisted
before master replies to client
● Master runs on a single thread – no
concurrency issues