This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Apache Spark is a fast, general engine for large-scale data processing. It provides unified analytics engine for batch, interactive, and stream processing using an in-memory abstraction called resilient distributed datasets (RDDs). Spark's speed comes from its ability to run computations directly on data stored in cluster memory and optimize performance through caching. It also integrates well with other big data technologies like HDFS, Hive, and HBase. Many large companies are using Spark for its speed, ease of use, and support for multiple workloads and languages.
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis MagdaDatabricks
It’s not enough to build a mesh of sensors or embedded devices to get more insights about the surrounding environment and optimize your production. Usually, your IoT solution needs to be capable of transferring enormous amounts of data to a storage or cloud where the data has to be processed further. Quite often, the processing of the endless streams of data has to be done almost in real-time so that you can react on the IoT subsystem’s state accordingly, and in time.
During this session, see how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite’s cluster resources. In particular, learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.
Productionizing Spark and the REST Job Server- Evan ChanSpark Summit
The document discusses productionizing Apache Spark and using the Spark REST Job Server. It provides an overview of Spark deployment options like YARN, Mesos, and Spark Standalone mode. It also covers Spark configuration topics like jars management, classpath configuration, and tuning garbage collection. The document then discusses running Spark applications in a cluster using tools like spark-submit and the Spark Job Server. It highlights features of the Spark Job Server like enabling low-latency Spark queries and sharing cached RDDs across jobs. Finally, it provides examples of using the Spark Job Server in production environments.
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng ShiDatabricks
This document discusses speeding up OLAP cube building in Apache Kylin using Spark. Cubing with MapReduce can be slow due to serialization overhead and repeated job submissions. Spark allows caching data in memory across cuboid layers in one job, significantly reducing build times compared to MapReduce as shown in a benchmark on a 160 million row dataset. Spark simplifies Kylin development and brings capabilities for real-time OLAP and cloud integration.
FiloDB - Breakthrough OLAP Performance with Cassandra and SparkEvan Chan
You want to ingest event, time-series, streaming data easily, yet have flexible, fast ad-hoc queries. Is this even possible? Yes! Find out how in this talk of combining Apache Cassandra and Apache Spark, using a new open-source database, FiloDB.
Alpine academy apache spark series #1 introduction to cluster computing wit...Holden Karau
Alpine academy apache spark series #1 introduction to cluster computing with python & a wee bit of scala. This is the first in the series and is aimed at the intro level, the next one will cover MLLib & ML.
1) Uber uses Spark and Hadoop to process large amounts of transportation data in real-time and batch. This includes building pipelines to ingest trip data from databases into a data warehouse within 1-2 hours.
2) Paricon is Uber's first Spark application which infers schemas from raw JSON data, converts it to Parquet format for faster querying, and validates the results. It processes over 15TB of data daily.
3) Future work includes building a SQL-based ETL platform on Spark, open sourcing SQL-on-Hadoop, and creating a machine learning platform with Spark and a real-time analytics system called Apollo using Spark Streaming.
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 integration.
Spark Summit EU talk by Steve LoughranSpark Summit
This document provides an overview of using Apache Spark with object stores like Amazon S3, Azure Blob Storage, and Google Cloud Storage. It discusses the key challenges of classpath configuration, credentials, code examples, and ensuring data consistency and durability. Specific tips are provided for configuring and working with S3 and Azure Blob Storage. The document emphasizes that object stores can be treated like any other URL, but some configuration is needed and performance/commitment challenges exist.
Spark as a Platform to Support Multi-Tenancy and Many Kinds of Data Applicati...Spark Summit
This document summarizes Uber's use of Spark as a data platform to support multi-tenancy and various data applications. Key points include:
- Uber uses Spark on YARN for resource management and isolation between teams/jobs. Parquet is used as the columnar file format for performance and schema support.
- Challenges include sharing infrastructure between many teams with different backgrounds and use cases. Spark provides a common platform.
- An Uber Development Kit (UDK) is used to help users get Spark jobs running quickly on Uber's infrastructure, with templates, defaults, and APIs for common tasks.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
This document summarizes a presentation about streaming data processing with Apache Flink. It discusses how Flink enables real-time analysis and continuous applications. Case studies are presented showing how companies like Bouygues Telecom, Zalando, King.com, and Netflix use Flink for applications like monitoring, analytics, and building a stream processing service. Flink performance is discussed through benchmarks, and features like consistent snapshots and dynamic scaling are mentioned.
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Apache Spark is rapidly emerging as the prime platform for advanced analytics in Hadoop. This briefing is updated to reflect news and announcements as of July 2014.
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
LLAP enables sub-second analytical queries in Hive by running query fragments directly in memory on compute nodes using a long-running daemon process. It provides high performance scans and execution through an in-memory columnar cache shared across queries. LLAP queries are coordinated independently by Tez while utilizing Hive operators for processing and Tez for data transfers. It improves upon traditional MapReduce and Tez by keeping intermediate query results in memory rather than writing to disk.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.
This document summarizes the work done by Yahoo engineers to optimize performance of queries on a mobile analytics data mart hosted on Apache Hive. They implemented several techniques like using Tez, vectorized query execution, map-side aggregations, and ORC file format, which provided significant performance boosts. For high cardinality partitioned tables, they leveraged sketching which reduced query times from over 100 seconds to under 25 seconds. They also implemented a data mart in a box solution for easier setup of custom data marts and funnels analysis using UDFs.
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Sqoop on Spark for Data Ingestion-(Veena Basavaraj and Vinoth Chandar, Uber)Spark Summit
This document discusses running Sqoop jobs on Apache Spark for faster data ingestion into Hadoop. The authors describe how Sqoop jobs can be executed as Spark jobs by leveraging Spark's faster execution engine compared to MapReduce. They demonstrate running a Sqoop job to ingest data from MySQL to HDFS using Spark and show it is faster than using MapReduce. Some challenges encountered are managing dependencies and job submission, but overall it allows leveraging Sqoop's connectors within Spark's distributed processing framework. Next steps include exploring alternative job submission methods in Spark and adding transformation capabilities to Sqoop connectors.
This document provides an introduction to Apache Spark and Zeppelin. It describes Spark as an open source cluster computing framework, and its APIs for Scala, Java, Python and R. Key Spark components are outlined like Spark Core, Spark SQL, MLlib and GraphX. RDDs are defined as Spark's primary abstraction, and DataFrames/Datasets are presented as higher-level APIs built on RDDs. The benefits of Spark SQL for structured data are highlighted. Examples demonstrate basic Spark and SQL usage. Finally, Apache Zeppelin and the Hortonworks sandbox are introduced as tools for interactive data analytics on Spark and Hadoop clusters.
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
This document provides an overview of installing and programming with Apache Spark on the Hortonworks Data Platform (HDP). It discusses how Spark fits within HDP and can be used for batch processing, streaming, SQL queries and machine learning. The document outlines how to install Spark on HDP using Ambari and describes Spark programming with Resilient Distributed Datasets (RDDs), transformations, actions and caching/persistence. It provides examples of Spark APIs and programming patterns.
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
http://hortonworks.com/hadoop/spark/
Recording:
https://hortonworks.webex.com/hortonworks/lsr.php?RCID=03debab5ba04b34a033dc5c2f03c7967
As the ratio of memory to processing power rapidly evolves, many within the Hadoop community are gravitating towards Apache Spark for fast, in-memory data processing. And with YARN, they use Spark for machine learning and data science use cases along side other workloads simultaneously. This is a continuation of our YARN Ready Series, aimed at helping developers learn the different ways to integrate to YARN and Hadoop. Tools and applications that are YARN Ready have been verified to work within YARN.
This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.
Achieving Mega-Scale Business Intelligence Through Speed of Thought Analytics...VMware Tanzu
SpringOne Platform 2016
Speaker: Ian Fyfe; Director, Product Marketing, Hortonworks
Apache Hadoop is the most powerful and popular platform for ingesting, storing and processing enormous amounts of “big data”. However, due to its original roots as a batch processing system, doing interactive business analytics with Hadoop has historically suffered from slow response times, or forced business analysts to extract data summaries out of Hadoop into separate data marts. This talk will discuss the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop including Apache Hive on Tez, Apache Hive on LLAP, Apache HAWQ and Apache MADlib.
Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.
Apache Spark: Lightning Fast Cluster ComputingAll Things Open
Apache Spark is an open-source cluster computing framework for fast and large-scale data processing. It uses an in-memory data abstraction called resilient distributed datasets (RDDs) that allow parallel operations on large datasets across a cluster. Spark also provides APIs in Java, Scala, Python and R for interactive data analysis through its core engine as well as high-level libraries for SQL, streaming, machine learning and graph processing.
This document provides information about running Spark on YARN including:
- Spark allows processing of large datasets in a distributed manner using Resilient Distributed Datasets (RDDs).
- When running on YARN, Spark is able to leverage existing Hadoop clusters for locality-aware processing, resource management, and other benefits while still using its own execution engine.
- Running Spark on YARN provides advantages like shipping code to where the data is located instead of moving large amounts of data, leveraging existing Hadoop cluster infrastructure, and allowing Spark workloads to run natively within Hadoop.
Hadoop Present - Open Enterprise HadoopYifeng Jiang
The document is a presentation on enterprise Hadoop given by Yifeng Jiang, a Solutions Engineer at Hortonworks. The presentation covers updates to Hadoop Core including HDFS and YARN, data access technologies like Hive, Spark and stream processing, security features in Hadoop, and Hadoop management with Apache Ambari.
This document discusses using Apache Spark with object stores like Amazon S3 and Microsoft Azure Blob Storage. It covers challenges around classpath configuration, credentials, code examples, and performance commitments when using these storage systems. Key points include using Hadoop connectors like S3A and WASB, configuring credentials through properties or environment variables, and tuning Spark for object store performance and consistency.
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
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Apache Tez - A unifying Framework for Hadoop Data ProcessingDataWorks Summit
This document provides an overview of Apache Tez, a framework for building data processing applications on Hadoop YARN. It describes how Tez allows applications to define complex data flows as directed acyclic graphs (DAGs) and handles distributed execution, fault tolerance, and resource management. Tez has improved the performance of Apache Hive and Pig by an order of magnitude by enabling more flexible DAG definitions and runtime optimizations. It also supports integration with other data processing engines like Spark, Storm and interactive SQL queries. The document outlines how Tez works and provides guidance on how developers can contribute to the open source project.
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...Data Con LA
Apache Tez is a library to build data processing engines in Hadoop/YARN. It takes care of many common building blocks like scheduling, fault tolerance, speculation, security etc. so that the engine can focus on its core features. E.g. Apache Hive can focus on SQL optimization. There has been rapid adoption in projects like Hive, Pig, Flink, Cascading, Scalding and commercial products like Datameer and Syncsort. We will provide a brief overview of Tez and then look at new features for job monitoring in the Tez UI and performance debugging tools for Tez applications. Finally we will explore upcoming features like hybrid scheduling that open up new areas of performance and functionality.
This document provides an agenda and overview for a presentation on deep learning on Hortonworks Data Platform (HDP). The presentation will cover using TensorFlow with Apache NiFi, running TensorFlow on YARN, using pre-built models with Apache MXNet, running an MXNet model server with NiFi, and running MXNet in Zeppelin notebooks and on YARN. It recommends installing CPU and GPU versions of frameworks on appropriate nodes and discusses options like TensorFlow, MXNet, and PyTorch. The document also outlines integrating Apache MXNet with NiFi for tasks like image classification using models on edge nodes or a model server.
The document discusses new features in Hive 2.0 including Hive LLAP (Live Long And Process) and Hive on ACID (Atomic, Consistent, Isolated, Durable). Hive LLAP introduces an in-memory caching mechanism that provides sub-second query performance for Hive. Hive on ACID allows for transactions on Hive tables including updates, deletes, and streaming ingestion while maintaining consistency and concurrency. The document provides overviews of how both features work and improvements they provide for analytics workloads on Hive.
SQL on Hadoop Batch, Interactive and Beyond.
Public Presentation showing history and where Hortonworks is looking to go with 100% Open Source Technology.
Apache Hive, Apache SparkSQL, Apache Pheonix, and Apache Druid
Similar to Intro to Big Data Analytics using Apache Spark and Apache Zeppelin (20)
In today's digital world, digital marketers are indispensable. They play a crucial role in helping businesses connect with their audiences effectively through various online channels. Whether you're considering a career change or aiming to advance in the field, here’s a detailed guide to thriving as a digital marketer in 2024.
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The Money Wave 2024 Review_ Is It the Key to Financial Success.pdfnirahealhty
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The Money Wave is a comprehensive financial program designed to equip individuals with the knowledge and tools necessary for achieving financial independence. It encompasses a range of resources, including educational materials, webinars, and community support, all aimed at helping users understand and leverage various financial opportunities.
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How Can Microsoft Office 365 Improve Your Productivity?Digital Host
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The Money Wave 2024 Review: Is It the Key to Financial Success?nirahealhty
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