A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...Amazon Web Services
In this session, we discuss our learnings from migrating the financial ledger and accounting system that Amazon uses from Oracle to AWS. We share the performance and cost benefits to enterprises who migrate critical systems from Oracle to AWS, the decision frameworks used to pick the appropriate AWS service for appropriate application, and best practices in project management.
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Etha...HostedbyConfluent
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Ethan Guo | Current 2022
Back in 2016, Apache Hudi brought transactions, change capture on top of data lakes, what is today referred to as the Lakehouse architecture. In this session, we first introduce Apache Hudi and the key technology gaps it fills in the modern data architecture. Bridging traditional data lakes and warehouses, Hudi helps realize the Lakehouse vision, by bringing transactions, optimized table metadata to data lakes and powerful storage layout optimizations, moving them closer to cloud warehouses of today. Viewed from a data engineering lens, Hudi also plays a key unifying role between the batch and stream processing worlds, by acting as a columnar, server-less ""state store"" for batch jobs, ushering in what we call the incremental processing model, where batch jobs can consume new data, update/delete intermediate results in a Hudi table, instead of re-computing/re-write entire output like old-school big batch jobs.
Rest of talk focusses on a deep dive into the some of the time-tested design choices and tradeoffs in Hudi, that helps power some of the largest transactional data lakes on the planet today. We will start by describing a tour of the storage format design, including data, metadata layouts and of course Hudi's timeline, an event log that is central to implementing ACID transactions and concurrency control. We will delve deeper into the practical concurrency control pitfalls in data lakes, and show how Hudi's hybrid approach combining MVCC with optimistic concurrency control, lowers contention and unlocks minute-level near real-time commits to Hudi tables. We will conclude with code examples that showcase Hudi's rich set of table services that perform vital table management such as cleaning older file versions, compaction of delta logs into base files, dynamic re-clustering for faster query performance, or the more recently introduced indexing service that maintains Hudi's multi-modal indexing capabilities.
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
Wellington Chevreuil of Cloudera
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...Amazon Web Services
In this session, we discuss our learnings from migrating the financial ledger and accounting system that Amazon uses from Oracle to AWS. We share the performance and cost benefits to enterprises who migrate critical systems from Oracle to AWS, the decision frameworks used to pick the appropriate AWS service for appropriate application, and best practices in project management.
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Etha...HostedbyConfluent
Designing Apache Hudi for Incremental Processing With Vinoth Chandar and Ethan Guo | Current 2022
Back in 2016, Apache Hudi brought transactions, change capture on top of data lakes, what is today referred to as the Lakehouse architecture. In this session, we first introduce Apache Hudi and the key technology gaps it fills in the modern data architecture. Bridging traditional data lakes and warehouses, Hudi helps realize the Lakehouse vision, by bringing transactions, optimized table metadata to data lakes and powerful storage layout optimizations, moving them closer to cloud warehouses of today. Viewed from a data engineering lens, Hudi also plays a key unifying role between the batch and stream processing worlds, by acting as a columnar, server-less ""state store"" for batch jobs, ushering in what we call the incremental processing model, where batch jobs can consume new data, update/delete intermediate results in a Hudi table, instead of re-computing/re-write entire output like old-school big batch jobs.
Rest of talk focusses on a deep dive into the some of the time-tested design choices and tradeoffs in Hudi, that helps power some of the largest transactional data lakes on the planet today. We will start by describing a tour of the storage format design, including data, metadata layouts and of course Hudi's timeline, an event log that is central to implementing ACID transactions and concurrency control. We will delve deeper into the practical concurrency control pitfalls in data lakes, and show how Hudi's hybrid approach combining MVCC with optimistic concurrency control, lowers contention and unlocks minute-level near real-time commits to Hudi tables. We will conclude with code examples that showcase Hudi's rich set of table services that perform vital table management such as cleaning older file versions, compaction of delta logs into base files, dynamic re-clustering for faster query performance, or the more recently introduced indexing service that maintains Hudi's multi-modal indexing capabilities.
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
Wellington Chevreuil of Cloudera
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...HostedbyConfluent
Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
The presentation begins with an overview of the growth of non-structured data and the benefits NoSQL products provide. It then provides an evaluation of the more popular NoSQL products on the market including MongoDB, Cassandra, Neo4J, and Redis. With NoSQL architectures becoming an increasingly appealing database management option for many organizations, this presentation will help you effectively evaluate the most popular NoSQL offerings and determine which one best meets your business needs.
Don’t optimize my queries, optimize my data!Julian Hyde
Your queries won't run fast if your data is not organized right. Apache Calcite optimizes queries, but can we evolve it so that it can optimize data? We had to solve several challenges. Users are too busy to tell us the structure of their database, and the query load changes daily, so Calcite has to learn and adapt.
We talk about new algorithms we developed for gathering statistics on massive database, and how we infer and evolve the data model based on the queries, suggesting materialized views that will make your queries run faster without you changing them.
A talk given by Julian Hyde at DataEngConf NYC, Columbia University, on 2017/10/30.
In this session, you get an overview of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service. We'll cover how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also discuss new features, architecture best practices, and share how customers are using Amazon Redshift for their Big Data workloads.
Take advantage of ScyllaDB’s wide column NoSQL features such as workload prioritization to balance the needs of OLTP and OLAP in the same cluster. Plus learn about the different compaction strategies and which one would be right for your workload. With additional insights on properly sizing your database and using open source tools for observability.
Introduction to Graph Databases with detailed installation steps, cypher query language examples, demos and visualization tools like RedisInsight. It also contains benchmarks for RedisGraph against Tigergraph, neo4j, neptune, Janusgraph and Arangodb. I mentions differences between native and non-native graph databases. It contains usecases for the graph databases and provides a score for selecting graph DB over traditional SQL and NoSQL DBs.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
Are you using the fastest query tool for Hadoop? Provide and discuss the latest performance results of the industry standard TPC_H benchmarks executed across an assortment of open source query tools such as Hive (using MR, TEZ, LLAP, SPARK), SparkSQL, Presto, and Drill. Additionally, the performance tests will utilize a variety of data sizes and popular storage formats such as ORC, Parquet and Text and compression codecs.
Oracle Open World (OOW) 2014 presentation on Oracle Cache Fusion; how it works and how to use it in an optimized fashion to scale an Oracle RAC system.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...HostedbyConfluent
Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
The presentation begins with an overview of the growth of non-structured data and the benefits NoSQL products provide. It then provides an evaluation of the more popular NoSQL products on the market including MongoDB, Cassandra, Neo4J, and Redis. With NoSQL architectures becoming an increasingly appealing database management option for many organizations, this presentation will help you effectively evaluate the most popular NoSQL offerings and determine which one best meets your business needs.
Don’t optimize my queries, optimize my data!Julian Hyde
Your queries won't run fast if your data is not organized right. Apache Calcite optimizes queries, but can we evolve it so that it can optimize data? We had to solve several challenges. Users are too busy to tell us the structure of their database, and the query load changes daily, so Calcite has to learn and adapt.
We talk about new algorithms we developed for gathering statistics on massive database, and how we infer and evolve the data model based on the queries, suggesting materialized views that will make your queries run faster without you changing them.
A talk given by Julian Hyde at DataEngConf NYC, Columbia University, on 2017/10/30.
In this session, you get an overview of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service. We'll cover how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also discuss new features, architecture best practices, and share how customers are using Amazon Redshift for their Big Data workloads.
Take advantage of ScyllaDB’s wide column NoSQL features such as workload prioritization to balance the needs of OLTP and OLAP in the same cluster. Plus learn about the different compaction strategies and which one would be right for your workload. With additional insights on properly sizing your database and using open source tools for observability.
Introduction to Graph Databases with detailed installation steps, cypher query language examples, demos and visualization tools like RedisInsight. It also contains benchmarks for RedisGraph against Tigergraph, neo4j, neptune, Janusgraph and Arangodb. I mentions differences between native and non-native graph databases. It contains usecases for the graph databases and provides a score for selecting graph DB over traditional SQL and NoSQL DBs.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
Are you using the fastest query tool for Hadoop? Provide and discuss the latest performance results of the industry standard TPC_H benchmarks executed across an assortment of open source query tools such as Hive (using MR, TEZ, LLAP, SPARK), SparkSQL, Presto, and Drill. Additionally, the performance tests will utilize a variety of data sizes and popular storage formats such as ORC, Parquet and Text and compression codecs.
Oracle Open World (OOW) 2014 presentation on Oracle Cache Fusion; how it works and how to use it in an optimized fashion to scale an Oracle RAC system.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
Interest is growing in the Apache Spark community in using Deep Learning techniques and in the Deep Learning community in scaling algorithms with Apache Spark. A few of them to note include:
· Databrick’s efforts in scaling Deep learning with Spark
· Intel announcing the BigDL: A Deep learning library for Spark
· Yahoo’s recent efforts to opensource TensorFlowOnSpark
In this lecture we will discuss the key use cases and developments that have emerged in the last year in using Deep Learning techniques with Spark.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
How to Use Apache Zeppelin with HWX HDBHortonworks
Part five in a five-part series, this webcast will be a demonstration of the integration of Apache Zeppelin and Pivotal HDB. Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. This webinar will demonstrate the configuration of the psql interpreter and the basic operations of Apache Zeppelin when used in conjunction with Hortonworks HDB.
Using Ansible to deploy a 6-node Hortonworks Data Platform (hadoop) cluster on AWS with the ObjectRocket ansible-hadoop playbook.
Presented at the Ansible NOVA MeetUp on February 23, 2017: https://www.meetup.com/Ansible-NOVA/events/236853616/
The path to a Modern Data Architecture in Financial ServicesHortonworks
Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.
Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.
H2O Distributed Deep Learning by Arno Candel 071614Sri Ambati
Deep Learning R Vignette Documentation: https://github.com/0xdata/h2o/tree/master/docs/deeplearning/
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Big Data Warehousing: Pig vs. Hive ComparisonCaserta
In a recent Big Data Warehousing Meetup in NYC, Caserta Concepts partnered with Datameer to explore big data analytics techniques. In the presentation, we made a Hive vs. Pig Comparison. For more information on our services or this presentation, please visit www.casertaconcepts.com or contact us at info (at) casertaconcepts.com.
http://www.casertaconcepts.com
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This webinar series covers Apache Kafka and Apache Storm for streaming data processing. Also, it discusses new streaming innovations for Kafka and Storm included in HDP 2.2
Similar to Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
1. Hadoop Demystified
What is it? How does Microsoft fit in?
and… of course… some demos!
Presentation for ATL .NET User Group
(July, 2014)
Lester Martin
Page 1
2. Agenda
• Hadoop 101
–Fundamentally, What is Hadoop?
–How is it Different?
–History of Hadoop
• Components of the Hadoop Ecosystem
• MapReduce, Pig, and Hive Demos
–Word Count
–Open Georgia Dataset Analysis
Page 2
3. Connection before Content
• Lester Martin
• Hortonworks – Professional Services
• lmartin@hortonworks.com
• http://about.me/lestermartin (links to blog, github, twitter, LI, FB, etc)
Page 3
5. The Need for Hadoop
• Store and use all types of data
• Process ALL the data; not just a sample
• Scalability to 1000s of nodes
• Commodity hardware
Page 5
6. Relational Database vs. Hadoop
Relational Hadoop
Required on write schema Required on Read
Reads are fast speed Writes are fast
Standards and structure governance Loosely structured
Limited, no data processing processing Processing coupled with data
Structured data types Multi and unstructured
Interactive OLAP Analytics
Complex ACID Transactions
Operational Data Store
best fit use Data Discovery
Processing unstructured data
Massive storage/processing
P
7. Fundamentally, a Simple Algorithm
1. Review stack of quarters
2. Count each year that ends
in an even number
Page 7
9. Distributed Algorithm – Map:Reduce
Page 9
Map
(total number of quarters)
Reduce
(sum each person’s total)
10. A Brief History of Apache Hadoop
Page 10
2013
Focus on INNOVATION
2005: Hadoop created
at Yahoo!
Focus on OPERATIONS
2008: Yahoo team extends focus to
operations to support multiple
projects & growing clusters
Yahoo! begins to
Operate at scale
Enterprise
Hadoop
Apache Project
Established
Hortonworks
Data Platform
2004 2008 2010 20122006
STABILITY
2011: Hortonworks created to focus on
“Enterprise Hadoop“. Starts with 24
key Hadoop engineers from Yahoo
12. HDP: Enterprise Hadoop Platform
Page 12
Hortonworks
Data Platform (HDP)
• The ONLY 100% open source
and complete platform
• Integrates full range of
enterprise-ready services
• Certified and tested at scale
• Engineered for deep
ecosystem interoperability
OS/VM Cloud Appliance
PLATFORM
SERVICES
HADOOP
CORE
Enterprise Readiness
High Availability, Disaster
Recovery, Rolling Upgrades,
Security and Snapshots
HORTONWORKS
DATA PLATFORM (HDP)
OPERATIONAL
SERVICES
DATA
SERVICES
HDFS
SQOOP
FLUME
NFS
LOAD &
EXTRACT
WebHDFS
KNOX*
OOZIE
AMBARI
FALCON*
YARN
MAP
TEZREDUCE
HIVE &
HCATALOG
PIGHBASE
15. Hive
• Data warehousing package built on top of Hadoop
• Bringing structure to unstructured data
• Query petabytes of data with HiveQL
• Schema on read
1
•
•
–
–
16. Hive: SQL-Like Interface to Hadoop
• Provides basic SQL functionality using MapReduce to
execute queries
• Supports standard SQL clauses
INSERT INTO
SELECT
FROM … JOIN … ON
WHERE
GROUP BY
HAVING
ORDER BY
LIMIT
• Supports basic DDL
CREATE/ALTER/DROP TABLE, DATABASE
Page 17
17. Hortonworks Investment
in Apache Hive
Batch AND Interactive SQL-IN-Hadoop
Stinger Initiative
A broad, community-based effort to
drive the next generation of HIVE
Page 18
Stinger Phase 3
• Hive on Apache Tez
• Query Service (always on)
• Buffer Cache
• Cost Based Optimizer (Optiq)
Stinger Phase 1:
• Base Optimizations
• SQL Types
• SQL Analytic Functions
• ORCFile Modern File Format
Stinger Phase 2:
• SQL Types
• SQL Analytic Functions
• Advanced Optimizations
• Performance Boosts via YARN
Speed
Improve Hive query performance by 100X to
allow for interactive query times (seconds)
Scale
The only SQL interface to Hadoop designed
for queries that scale from TB to PB
Goals:
…70% complete
in 6 months…all IN Hadoop
SQL
Support broadest range of SQL semantics for
analytic applications running against Hadoop
18. Stinger: Enhancing SQL Semantics
Page 19
Hive SQL Datatypes Hive SQL Semantics
INT SELECT, LOAD, INSERT from query
TINYINT/SMALLINT/BIGINT Expressions in WHERE and HAVING
BOOLEAN GROUP BY, ORDER BY, SORT BY
FLOAT Sub-queries in FROM clause
DOUBLE GROUP BY, ORDER BY
STRING CLUSTER BY, DISTRIBUTE BY
TIMESTAMP ROLLUP and CUBE
BINARY UNION
DECIMAL LEFT, RIGHT and FULL INNER/OUTER JOIN
ARRAY, MAP, STRUCT, UNION CROSS JOIN, LEFT SEMI JOIN
CHAR Windowing functions (OVER, RANK, etc.)
VARCHAR INTERSECT, EXCEPT, UNION DISTINCT
DATE Sub-queries in HAVING
Sub-queries in WHERE (IN/NOT IN,
EXISTS/NOT EXISTS
Hive 0.10
Hive 12
Hive 0.11
Compete Subset
Hive 13
19. Pig
• Pig was created at Yahoo! to analyze data in HDFS without writing
Map/Reduce code.
• Two components:
– SQL like processing language called “Pig Latin”
– PIG execution engine producing Map/Reduce code
• Popular uses:
– ETL at scale (offloading)
– Text parsing and processing to Hive or HBase
– Aggregating data from multiple sources
•
•
•
20. Pig
Sample Code to find dropped call data:
4G_Data = LOAD ‘/archive/FDR_4G.txt’ using TextLoader();
Customer_Master = LOAD ‘masterdb.customer_data’ using
HCatLoader();
4G_Data_Full = JOIN 4G_Data by customerID, CustomerMaster by
customerID;
X = FILTER 4G_Data_Full BY State == ‘call_dropped’;
•
•
•
22. Powering the Modern Data Architecture
HADOOP 2.0
Multi Use Data Platform
Batch, Interactive, Online, Streaming, …
Page 23
Interact with all data in
multiple ways simultaneously
Redundant, Reliable Storage
HDFS 2
Cluster Resource Management
YARN
Standard SQL
Processing
Hive
Batch
MapReduce
Interactive
Tez
Online Data
Processing
HBase, Accumulo
Real Time Stream
Processing
Storm
others
…
HADOOP 1.0
HDFS 1
(redundant, reliable storage)
MapReduce
(distributed data processing
& cluster resource management)
Single Use System
Batch Apps
Data Processing
Frameworks
(Hive, Pig, Cascading, …)
23. Word Counting Time!!
Hadoop’s “Hello Whirled” Example
A quick refresher of core elements of
Hadoop and then code walk-thrus with
Java MapReduce and Pig
Page 25
24. Core Hadoop Concepts
• Applications are written in high-level code
–Developers need not worry about network programming, temporal
dependencies or low-level infrastructure
• Nodes talk to each other as little as possible
–Developers should not write code which communicates between
nodes
–“Shared nothing” architecture
• Data is spread among machines in advance
–Computation happens where the data is stored, wherever possible
– Data is replicated multiple times on the system for increased
availability and reliability
Page 26
25. Hadoop: Very High-Level Overview
• When data is loaded in the system, it is split into
“blocks”
–Typically 64MB or 128MB
• Map tasks (first part of MapReduce) work on relatively
small portions of data
–Typically a single block
• A master program allocates work to nodes such that a
Map tasks will work on a block of data stored locally
on that node whenever possible
–Many nodes work in parallel, each on their own part of the overall
dataset
Page 27
26. Fault Tolerance
• If a node fails, the master will detect that failure and
re-assign the work to a different node on the system
• Restarting a task does not require communication
with nodes working on other portions of the data
• If a failed node restarts, it is automatically added back
to the system and assigned new tasks
• If a nodes appears to be running slowly, the master
can redundantly execute another instance of the same
task
–Results from the first to finish will be used
–Known as “speculative execution”
Page 28
27. Hadoop Components
• Hadoop consists of two core components
–The Hadoop Distributed File System (HDFS)
–MapReduce
• Many other projects based around core Hadoop (the
“Ecosystem”)
–Pig, Hive, Hbase, Flume, Oozie, Sqoop, Datameer, etc
• A set of machines running HDFS and MapReduce is
known as a Hadoop Cluster
–Individual machines are known as nodes
–A cluster can have as few as one node, as many as several
thousand
– More nodes = better performance!
Page 29
28. Hadoop Components: HDFS
• HDFS, the Hadoop Distributed File System, is
responsible for storing data on the cluster
• Data is split into blocks and distributed across
multiple nodes in the cluster
–Each block is typically 64MB (the default) or 128MB in size
• Each block is replicated multiple times
–Default is to replicate each block three times
–Replicas are stored on different nodes
– This ensures both reliability and availability
Page 30
30. HDFS *is* a File System
• Screenshot for “Name Node UI”
Page 32
31. Accessing HDFS
• Applications can read and write HDFS files directly via
a Java API
• Typically, files are created on a local filesystem and
must be moved into HDFS
• Likewise, files stored in HDFS may need to be moved
to a machine’s local filesystem
• Access to HDFS from the command line is achieved
with the hdfs dfs command
–Provides various shell-like commands as you find on Linux
–Replaces the hadoop fs command
• Graphical tools available like the Sandbox’s Hue File
Browser and Red Gate’s HDFS Explorer
Page 33
32. hdfs dfs Examples
• Copy file foo.txt from local disk to the user’s directory
in HDFS
–This will copy the file to /user/username/fooHDFS.txt
• Get a directory listing of the user’s home directory in
HDFS
• Get a directory listing of the HDFS root directory
Page 34
hdfs dfs –put fooLocal.txt fooHDFS.txt
hdfs dfs –ls
hdfs dfs –ls /
33. hdfs dfs Examples (continued)
• Display the contents of a specific HDFS file
• Move that file back to the local disk
• Create a directory called input under the user’s home
directory
• Delete the HDFS directory input and all its contents
Page 35
hdfs dfs –cat /user/fred/fooHDFS.txt
hdfs dfs –mkdir input
hdfs dfs –rm –r input
hdfs dfs –get /user/fred/fooHDFS.txt barLocal.txt
34. Hadoop Components: MapReduce
• MapReduce is the system used to process data in the
Hadoop cluster
• Consists of two phases: Map, and then Reduce
–Between the two is a stage known as the shuffle and sort
• Each Map task operates on a discrete portion of the
overall dataset
–Typically one HDFS block of data
• After all Maps are complete, the MapReduce system
distributes the intermediate data to nodes which
perform the Reduce phase
–Source code examples and live demo coming!
Page 36
35. Features of MapReduce
• Hadoop attempts to run tasks on nodes which hold
their portion of the data locally, to avoid network
traffic
• Automatic parallelization, distribution, and fault-
tolerance
• Status and monitoring tools
• A clean abstraction for programmers
–MapReduce programs are usually written in Java
– Can be written in any language using Hadoop Streaming
– All of Hadoop is written in Java
–With “housekeeping” taken care of by the framework, developers
can concentrate simply on writing Map and Reduce functions
Page 37
38. MapReduce: The Mapper
• The Mapper reads data in the form of key/value pairs
(KVPs)
• It outputs zero or more KVPs
• The Mapper may use or completely ignore the input
key
–For example, a standard pattern is to read a line of a file at a time
– The key is the byte offset into the file at which the line starts
– The value is the contents of the line itself
– Typically the key is considered irrelevant with this pattern
• If the Mapper writes anything out, it must in the form
of KVPs
–This “intermediate data” is NOT stored in HDFS (local storage only
without replication)
Page 40
39. MapReducer: The Reducer
• After the Map phase is over, all the intermediate
values for a given intermediate key are combined
together into a list
• This list is given to a Reducer
–There may be a single Reducer, or multiple Reducers
–All values associated with a particular intermediate key are
guaranteed to go to the same Reducer
–The intermediate keys, and their value lists, are passed in sorted
order
• The Reducer outputs zero or more KVPs
–These are written to HDFS
–In practice, the Reducer often emits a single KVP for each input
key
Page 41
40. MapReduce Example: Word Count
• Count the number of occurrences of each word in a
large amount of input data
Page 42
map(String input_key, String input_value)
foreach word in input_value:
emit(w,1)
reduce(String output_key, Iter<int> intermediate_vals)
set count = 0
foreach val in intermediate_vals:
count += val
emit(output_key, count)
41. MapReduce Example: Map Phase
Page 43
• Input to the Mapper
• Ignoring the key
– It is just an offset
• Output from the Mapper
• No attempt is made to optimize
within a record in this example
– This is a great use case for a
“Combiner”
(8675, ‘I will not eat
green eggs and ham’)
(8709, ‘I will not eat
them Sam I am’)
(‘I’, 1), (‘will’, 1),
(‘not’, 1), (‘eat’, 1),
(‘green’, 1), (‘eggs’, 1),
(‘and’, 1), (‘ham’, 1),
(‘I’, 1), (‘will’, 1),
(‘not’, 1), (‘eat’, 1),
(‘them’, 1), (‘Sam’, 1),
(‘I’, 1), (‘am’, 1)
42. MapReduce Example: Reduce Phase
Page 44
• Input to the Reducer
• Notice keys are sorted and
associated values for same key
are in a single list
– Shuffle & Sort did this for us
• Output from the Reducer
• All done!
(‘I’, [1, 1, 1])
(‘Sam’, [1])
(‘am’, [1])
(‘and’, [1])
(‘eat’, [1, 1])
(‘eggs’, [1])
(‘green’, [1])
(‘ham’, [1])
(‘not’, [1, 1])
(‘them’, [1])
(‘will’, [1, 1])
(‘I’, 3)
(‘Sam’, 1)
(‘am’, 1)
(‘and’, 1)
(‘eat’, 2)
(‘eggs’, 1)
(‘green’, 1)
(‘ham’, 1)
(‘not’, 2)
(‘them’, 1)
(‘will’, 2)
43. Code Walkthru & Demo Time!!
• Word Count Example
–Java MapReduce
–Pig
Page 45
45. Dataset: Open Georgia
• Salaries & Travel Reimbursements
–Organization
– Local Boards of Education
– Several Atlanta-area districts; multiple years
– State Agencies, Boards, Authorities and Commissions
– Dept of Public Safety; 2010
Page 47
46. Format & Sample Data
Page 48
NAME (String) TITLE (String)
SALARY
(float)
ORG TYPE
(String)
ORG (String) YEAR (int)
ABBOTT,DEEDEE W
GRADES 9-12
TEACHER
52,122.10 LBOE
ATLANTA INDEPENDENT
SCHOOL SYSTEM
2010
ALLEN,ANNETTE D
SPEECH-LANGUAGE
PATHOLOGIST
92,937.28 LBOE
ATLANTA INDEPENDENT
SCHOOL SYSTEM
2010
BAHR,SHERREEN T GRADE 5 TEACHER 52,752.71 LBOE
COBB COUNTY SCHOOL
DISTRICT
2010
BAILEY,ANTOINETT
E R
SCHOOL
SECRETARY/CLERK
19,905.90 LBOE
COBB COUNTY SCHOOL
DISTRICT
2010
BAILEY,ASHLEY N
EARLY INTERVENTION
PRIMARY TEACHER
43,992.82 LBOE
COBB COUNTY SCHOOL
DISTRICT
2010
CALVERT,RONALD
MARTIN
STATE PATROL (SP) 51,370.40 SABAC
PUBLIC SAFETY, DEPARTMENT
OF
2010
CAMERON,MICHAE
L D
PUBLIC SAFETY TRN
(AL)
34,748.60 SABAC
PUBLIC SAFETY, DEPARTMENT
OF
2010
DAAS,TARWYN
TARA
GRADES 9-12
TEACHER
41,614.50 LBOE
FULTON COUNTY BOARD OF
EDUCATION
2011
DABBS,SANDRA L
GRADES 9-12
TEACHER
79,801.59 LBOE
FULTON COUNTY BOARD OF
EDUCATION
2011
E'LOM,SOPHIA L
IS PERSONNEL -
GENERAL ADMIN
75,509.00 LBOE
FULTON COUNTY BOARD OF
EDUCATION
2012
EADDY,FENNER R SUBSTITUTE 13,469.00 LBOE
FULTON COUNTY BOARD OF
EDUCATION
2012
EADY,ARNETTA A ASSISTANT PRINCIPAL 71,879.00 LBOE
FULTON COUNTY BOARD OF
EDUCATION
2012
47. Simple Use Case
• For all loaded State of Georgia salary information
–Produce statistics for each specific job title
– Number of employees
– Salary breakdown
– Minimum
– Maximum
– Average
–Limit the data to investigate
– Fiscal year 2010
– School district employees
Page 49
48. Code Walkthru & Demo; Part Deux!
• Word Count Example
–Java MapReduce
–Pig
–Hive
Page 50
49. Demo Wrap-Up
• All code, test data, wiki pages, and blog posting can
be found, or linked to, from
–https://github.com/lestermartin/hadoop-exploration
• This deck can be found on SlideShare
–http://www.slideshare.net/lestermartin
• Questions?
Page 51
50. Thank You!!
• Lester Martin
• Hortonworks – Professional Services
• lmartin@hortonworks.com
• http://about.me/lestermartin (links to blog, github, twitter, LI, FB, etc)
Page 52
Editor's Notes
Hadoop fills several important needs in your data storage and processing infrastructure
Store and use all types of data: Allows semi-structured, unstructured and structured data to be processed in a way to create new insights of significant business value.
Process all the data: Instead of looking at samples of data or small sections of data, organizations can look at large volumes of data to get new perspective and make business decisions with higher degree of accuracy.
Scalability: Reducing latency in business is critical for success. The massive scalability of Big Data systems allow organizations to process massive amounts of data in a fraction of the time required for traditional systems.
Commodity hardware: Self-healing, extremely scalable, highly available environment with cost-effective commodity hardware.
KEY CALLOUT: Schema on Read
IMPORTANT NOTE: Hadoop is not meant to replace your relational database. Hadoop is for storing Big Data, which is often the type of data that you would otherwise not store in a database due to size or cost constraints You will still have your database for relational, transactional data.
I can’t really talk about Hortonworks without first taking a moment to talk about the history of Hadoop.
What we now know of as Hadoop really started back in 2005, when the team at yahoo! – started to work on a project that to build a large scale data storage and processing technology that would allow them to store and process massive amounts of data to underpin Yahoo’s most critical application, Search. The initial focus was on building out the technology – the key components being HDFS and MapReduce – that would become the Core of what we think of as Hadoop today, and continuing to innovate it to meet the needs of this specific application.
By 2008, Hadoop usage had greatly expanded inside of Yahoo, to the point that many applications were now using this data management platform, and as a result the team’s focus extended to include a focus on Operations: now that applications were beginning to propagate around the organization, sophisticated capabilities for operating it at scale were necessary. It was also at this time that usage began to expand well beyond Yahoo, with many notable organizations (including Facebook and others) adopting Hadoop as the basis of their large scale data processing and storage applications and necessitating a focus on operations to support what as by now a large variety of critical business applications.
In 2011, recognizing that more mainstream adoption of Hadoop was beginning to take off and with an objective of facilitating it, the core team left – with the blessing of Yahoo – to form Hortonworks. The goal of the group was to facilitate broader adoption by addressing the Enterprise capabilities that would would enable a larger number of organizations to adopt and expand their usage of Hadoop.
[note: if useful as a talk track, Cloudera was formed in 2008 well BEFORE the operational expertise of running Hadoop at scale was established inside of Yahoo]
SQL is a query language
Declarative, what not how
Oriented around answering a question
Requires uniform schema
Requires metadata
Known by everyone
A great choice for answering queries, building reports, use with automated tools
With Hive and Stinger we are focused on enabling the SQL ecosystem and to do that we’ve put Hive on a clear roadmap to SQL compliance.
That includes adding critical datatypes like character and date types as well as implementing common SQL semantics seen in most databases.
“hdfs dfs” is the *new* “hadoop fs”
Blank acts like ~
These two slides were just to make folks feel at home with CLI access to HDFS
See https://martin.atlassian.net/wiki/x/FwAvAQ for more details
Surely not the typical Volume/Velocity/Variety definition of “Big Data”, but gives us a controlled environment to do some simple prototyping and validating with
See https://martin.atlassian.net/wiki/x/NYBmAQ for more details
See https://martin.atlassian.net/wiki/x/FwAvAQ for more information