Alternatives to Apache Accumulo’s Java APIJosh Elser
Talk from Accumulo Summit 2015. Covers other projects which allows interactions with Apache Accumulo. Defines a model for why you would choose one over another and evaluates the strengths and weaknesses of each.
7:30 SQL-on-Accumulo - Don Miner, ClearEdge IT
Running SQL queries over data in Accumulo is easier said than done and has several nuanced design challenges that don't have clear answers. This talk will give an outline of the current state of the art in SQL-on-Accumulo technologies, while giving a realistic view on what is doable and what is not doable today.
This was presented for an O'Reilly Media webcast. http://www.oreilly.com/pub/e/3152?cmp=tw-na-webcast-product-webcast_an_introduction_to_apache_accumulo
This webcast will cover the basics of Apache Accumulo architecture and how it works, along with examples of how it is used. We'll also talk about some interesting use cases, such as text indexing, fine-grained multi-level access controls, and storing large-scale graphs. We'll also briefly touch on what sets Accumulo apart from other similar and not-so similar systems and where we think the Accumulo project is headed in a technical direction.
A description of Accumulo from the Apache Accumulo website:
The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
Marcel Kornacker is a tech lead at Cloudera
In this talk from Impala architect Marcel Kornacker, you will explore: How Impala's architecture supports query speed over Hadoop data that not only convincingly exceeds that of Hive, but also that of a proprietary analytic DBMS over its own native columnar format. The current state of, and roadmap for, Impala's analytic SQL functionality. An example configuration and benchmark suite that demonstrate how Impala offers a high level of performance, functionality, and ability to handle a multi-user workload, while retaining Hadoop’s traditional strengths of flexibility and ease of scaling.
James Kinley from Cloudera:
An introduction to Cloudera Impala. Cloudera Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase. In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive. This provides a familiar and unified platform for batch-oriented or real-time queries.
The link to the video: http://zurichtechtalks.ch/post/37339409724/an-introduction-to-cloudera-impala-sql-on-top-of
Alternatives to Apache Accumulo’s Java APIJosh Elser
Talk from Accumulo Summit 2015. Covers other projects which allows interactions with Apache Accumulo. Defines a model for why you would choose one over another and evaluates the strengths and weaknesses of each.
7:30 SQL-on-Accumulo - Don Miner, ClearEdge IT
Running SQL queries over data in Accumulo is easier said than done and has several nuanced design challenges that don't have clear answers. This talk will give an outline of the current state of the art in SQL-on-Accumulo technologies, while giving a realistic view on what is doable and what is not doable today.
This was presented for an O'Reilly Media webcast. http://www.oreilly.com/pub/e/3152?cmp=tw-na-webcast-product-webcast_an_introduction_to_apache_accumulo
This webcast will cover the basics of Apache Accumulo architecture and how it works, along with examples of how it is used. We'll also talk about some interesting use cases, such as text indexing, fine-grained multi-level access controls, and storing large-scale graphs. We'll also briefly touch on what sets Accumulo apart from other similar and not-so similar systems and where we think the Accumulo project is headed in a technical direction.
A description of Accumulo from the Apache Accumulo website:
The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
Marcel Kornacker is a tech lead at Cloudera
In this talk from Impala architect Marcel Kornacker, you will explore: How Impala's architecture supports query speed over Hadoop data that not only convincingly exceeds that of Hive, but also that of a proprietary analytic DBMS over its own native columnar format. The current state of, and roadmap for, Impala's analytic SQL functionality. An example configuration and benchmark suite that demonstrate how Impala offers a high level of performance, functionality, and ability to handle a multi-user workload, while retaining Hadoop’s traditional strengths of flexibility and ease of scaling.
James Kinley from Cloudera:
An introduction to Cloudera Impala. Cloudera Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase. In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive. This provides a familiar and unified platform for batch-oriented or real-time queries.
The link to the video: http://zurichtechtalks.ch/post/37339409724/an-introduction-to-cloudera-impala-sql-on-top-of
With the public confession of Facebook, HBase is on everyone's lips when it comes to the discussion around the new "NoSQL" area of databases. In this talk, Lars will introduce and present a comprehensive overview of HBase. This includes the history of HBase, the underlying architecture, available interfaces, and integration with Hadoop.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Impala Architecture Presentation at Toronto Hadoop User Group, in January 2014 by Mark Grover.
Event details:
http://www.meetup.com/TorontoHUG/events/150328602/
January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network
Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016.
http://www.meetup.com/SF-Data-Engineering/events/228293610/
Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases?
What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture.
This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer toolsYahoo Developer Network
Apache Sqoop 2 is the next generation of the massively successful open source tool designed to transfer data between traditional SQL databases and warehouses into Apache Hadoop. Sqoop 2 is designed as a client-server system with a repository which stores connection and job information. Sqoop 2 is designed to support secure job submission and multiple different roles for users. In this talk, we will discuss the issues users faced in Sqoop 1, and the design of Sqoop 2 and how the issues faced in Sqoop 1 are being handled in Sqoop 2.
Presenter(s): Hari Shreedharan, Software Engineer, Cloudera
Slides for presentation on Cloudera Impala I gave at the DC/NOVA Java Users Group on 7/9/2013. It is a slightly updated set of slides from the ones I uploaded a few months ago on 4/19/2013. It covers version 1.0.1 and also includes some new slides on HortonWorks' Stinger Initiative.
Kudu: Resolving Transactional and Analytic Trade-offs in Hadoopjdcryans
Presentation given on October 22nd, 2015, at the SF Spark and Friends meetup hosted by Quantcast. A recording should be available soon on the meetup's page: http://www.meetup.com/SF-Spark-and-Friends/events/226023299/
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
With the public confession of Facebook, HBase is on everyone's lips when it comes to the discussion around the new "NoSQL" area of databases. In this talk, Lars will introduce and present a comprehensive overview of HBase. This includes the history of HBase, the underlying architecture, available interfaces, and integration with Hadoop.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Impala Architecture Presentation at Toronto Hadoop User Group, in January 2014 by Mark Grover.
Event details:
http://www.meetup.com/TorontoHUG/events/150328602/
January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transacti...Yahoo Developer Network
Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016.
http://www.meetup.com/SF-Data-Engineering/events/228293610/
Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases?
What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture.
This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer toolsYahoo Developer Network
Apache Sqoop 2 is the next generation of the massively successful open source tool designed to transfer data between traditional SQL databases and warehouses into Apache Hadoop. Sqoop 2 is designed as a client-server system with a repository which stores connection and job information. Sqoop 2 is designed to support secure job submission and multiple different roles for users. In this talk, we will discuss the issues users faced in Sqoop 1, and the design of Sqoop 2 and how the issues faced in Sqoop 1 are being handled in Sqoop 2.
Presenter(s): Hari Shreedharan, Software Engineer, Cloudera
Slides for presentation on Cloudera Impala I gave at the DC/NOVA Java Users Group on 7/9/2013. It is a slightly updated set of slides from the ones I uploaded a few months ago on 4/19/2013. It covers version 1.0.1 and also includes some new slides on HortonWorks' Stinger Initiative.
Kudu: Resolving Transactional and Analytic Trade-offs in Hadoopjdcryans
Presentation given on October 22nd, 2015, at the SF Spark and Friends meetup hosted by Quantcast. A recording should be available soon on the meetup's page: http://www.meetup.com/SF-Spark-and-Friends/events/226023299/
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Cloudera Operational DB (Apache HBase & Apache Phoenix)Timothy Spann
Cloudera Operational DB (Apache HBase & Apache Phoenix)
Using Apache NiFi 1.10 to read/write from HBase
Dec 2019, Timothy Spann, Field Engineer, Data in Motion
Princeton Meetup 10-dec-2019
https://www.meetup.com/futureofdata-princeton/events/266496424/
Hosted By PGA Fund at:
https://pga.fund/coworking-space/
Princeton Growth Accelerator
5 Independence Way, 4th Floor, Princeton, NJ
Hive 3 New Horizons DataWorks Summit Melbourne February 2019alanfgates
Hive 3 new SQL features including LLAP, workload management, SQL over Kafka and JDBC data sources, integration with Spark via Hive Warehouse Connector, ACID 2, and constraints and default values
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Speaker: Alan Gates, Co-Founder, Hortonworks
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Apache Kudu (Incubating): New Hadoop Storage for Fast Analytics on Fast Data ...Cloudera, Inc.
The Hadoop ecosystem has improved real-time access capabilities recently, narrowing the gap with relational database technologies. However, gaps remain in the storage layer that complicate the transition to Hadoop-based architectures. In this session, the presenter will describe these gaps and discuss the tradeoffs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. The session also will cover Kudu (currently in beta), the new addition to the open source Hadoop ecosystem with outof-the-box integration with Apache Spark and Apache Impala (incubating), that achieves fast scans and fast random access from a single API.
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Apache HBase™ is the Hadoop database, a distributed, salable, big data store.Its a column-oriented database management system that runs on top of HDFS.
Apache HBase is an open source NoSQL database that provides real-time read/write access to those large data sets. ... HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today.
The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact.
The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories:
Data Lifecycle Connection
Data for Enterprise AI
Cloud Innovation
Security & Governance Leadership
People First
Data for Good
Industry Transformation
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
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Session Overview
-------------------------------------------
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FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
HBase and Accumulo | Washington DC Hadoop User Group
1. HBase and Accumulo
Washington DC Hadoop User Group
Jan 25th, 2012
Todd Lipcon
Software Engineer, Cloudera
todd@cloudera.com / @tlipcon
Copyright 2011 Cloudera Inc. All rights reserved
2. Background – Overview
• HBase and Accumulo are both open-source, Apache
2.0 licensed implementations of Google’s BigTable
infrastructure, running on Apache Hadoop
• Scalable, distributed storage
• Scalable data storage at petabyte scale, storing trillions of
rows distributed across hundreds or thousands of machines
• Automatic fault tolerance and data distribution as machines
crash or rejoin the cluster
• Linear scaling of IOPS and data capacity by adding servers
• Data model is a big sorted hierarchical map
Copyright 2012 Cloudera Inc. All rights reserved 2
3. Sorted Map Datastores
• Each row has a row key (like a Primary Key in RDBMS
terms)
• Users may query by exact row key or by range of row keys
• Data is always stored and returned in sorted order
• Each row has some number of columns
• Each column has a qualifier and some piece of data. Like a
Map<byte[], byte[]>
• Different rows may have different sets of columns
• Each cell has an associated timestamp and may retain a
history of previous values
• Columns are grouped into column families and locality
groups
Copyright 2012 Cloudera Inc. All rights reserved 3
4. Sorted Map Datastore
(logical view as “records”)
Implicit PRIMARY KEY in
RDBMS terms Data is all byte[] in HBase
Row key Data
Different types of
data separated into
cutting info: , ‘height’: ‘9ft’, ‘state’: ‘CA’ -
different roles: , ‘ASF’: ‘Director’, ‘Hadoop’: ‘Founder’ -
“column families” tlipcon info: , ‘height’: ‘5ft7, ‘state’: ‘CA’ -
roles: , ‘Hadoop’: ‘Committer’@ts=2010,
‘Hadoop’: ‘PMC’@ts=2011,
‘Hive’: ‘Contributor’ -
Different rows may have different sets A single cell might have different
of columns(table is sparse) values at different timestamps
Useful for *-To-Many mappings
5. Locality Groups
• Different sets of columns may have different properties
and access patterns
• Perhaps a few columns are accessed all the time, whereas
others are large and rarely needed
• For example, a user’s metadata (1kb, accessed frequently) and
their photo (1MB, cached by CDN and accessed rarely)
• Put metadata in one locality group and photos in
another
• Locality groups stored separately on disk: access just the
metadata without reading the photo
6. Sorted Map Datastore
(physical view as “cells”)
info Column Family / Locality Group
Row key Column key Timestamp Cell value
cutting info:height 1273516197868 9ft
cutting info:state 1043871824184 CA
tlipcon info:height 1273878447049 5ft7
tlipcon info:state 1273616297446 CA
roles Column Family / Locality Group
Row key Column key Timestamp Cell value
cutting roles:ASF 1273871823022 Director
Sorted
on disk by cutting roles:Hadoop 1183746289103 Founder
Row key, Col tlipcon roles:Hadoop 1300062064923 PMC
key,
descending tlipcon roles:Hadoop 1293388212294 Committer
timestamp
tlipcon roles:Hive 1273616297446 Contributor
Milliseconds since unix epoch
8. Accumulo/HBase Terminology
Accumulo HBase Definition
Tablet Region A partition of a table (eg email inboxes starting
with ‘a’-’c’)
TabletServer RegionServer A server in the cluster which hosts a number of
tablets/regions, providing read/write access
Log/WAL HLog/WAL Write-ahead log – used for durably logging edits
Minor Flush Writing data from memory to disk
compaction
Major Minor Merging several on-disk files into a larger one
compaction Compaction
Major Major Merging all of the on-disk files into a larger one
compaction compaction
with all files
Copyright 2012 Cloudera Inc. All rights reserved 8
9. That’s all the intro we have time for…
• Check out the excellent Accumulo manual at
http://incubator.apache.org/accumulo
• And the HBase manual at
http://hbase.apache.org/book.html
• Also some longer intro videos on Cloudera’s website,
and an excellent O’Reilly book
Copyright 2012 Cloudera Inc. All rights reserved 9
10. Commonalities (the non-controversial stuff)
• Both systems scale well
• Clusters with >1000 nodes, >1PB
• Example HBase users: StumbleUpon, TrendMicro, Facebook,
eBay, Flurry, ngmoco, Mozilla, Adobe, etc.
• Example Accumulo users: ??????? (I don’t have clearance but
I’m told they’re big and important)
• Both systems perform well
• Depending on tuning, one might beat the other at any given
benchmark, but overall results seem comparable
• Both open source with active development
Copyright 2012 Cloudera Inc. All rights reserved 10
11. Commonalities (the non-controversial stuff)
• Storage formats are very similar
• Used to be the same, then diverged, then re-converged!
• Multi-level BTrees, bloom filters, compression
• Prefix compression currently missing in HBase, 95% complete
for 0.94.0
• Caching code very similar
• Accumulo uses an older version of HBase’s LRUBlockCache
• HBase has some recent improvements (off-heap cache), but I
imagine Accumulo will grab them soon enough.
Copyright 2012 Cloudera Inc. All rights reserved 11
12. General features
• Both have good MapReduce integration
• Both have a command-line shell
• Both have a pretty good test suite
• Accumulo used to be ahead here, but we traded off some
ideas and use similar testing strategies now
• Both use ZooKeeper for fault tolerant metadata storage,
and support failover Masters
Copyright 2012 Cloudera Inc. All rights reserved 12
13. Now for the fun part… BigTable shootout 2012
• Warning: I am necessarily biased as an HBase
committer.
• I will be comparing the very latest versions
• HBase 0.92.0 (released only 2 days ago!)
• Accumulo 1.4 (not yet released, due out mid Feb?)
• Please feel free to loudly disagree after the talk during
the time allotted for questions – I am happy to be
proven wrong! I’ll invite Aaron Cordova and John Vines
up to help answer questions.
Copyright 2012 Cloudera Inc. All rights reserved 13
14. Differences – Active contributors and users
(plus various contractors thereof)
(I ran out of space)
Copyright 2012 Cloudera Inc. All rights reserved 14
15. Differences – User Mailing list activity
500-600 messages 50-100 messages
per month (peak per month (peak
1088) 105)
*but it’s new at Apache+
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 15
16. Differences – Access Control
• Accumulo has per-cell visibility labels as well as table
ACLs
• Each cell has an ACL of what users may see it. (eg
(TS|(SECRET&PROJECTX)))
• Users who don’t have access can’t tell the cell even exists
• Very useful for classified information!
• HBase has column family ACLs but no built-in per-cell
visibility support
• Some early work to add visibility labels, but not done yet
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 16
17. Differences – Authentication
• Accumulo has a built-in user database
• Users are authenticated by username/password
• Passed in plaintext over the wire
• HBase optionally uses Kerberos
• Central administration (eg via Active Directory)
• Key-based secure credential exchange
• Temporary delegation tokens are created for MR jobs, so even
if a job’s data leaks, credentials are not compromised
• Consistent with rest of Hadoop ecosystem
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 17
18. Differences – Locality Groups
• HBase has a 1:1 correspondence of Column Families
and Locality Groups
• Moving columns from one locality group to another after data
has been inserted is impossible
• Accumulo has a proper distinction and allows online
reassignment of column-to-locality-group mappings
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 18
19. Differences – extensibility frameworks
• Accumulo has iterators
• Allows custom processing to be inserted in the read path as
well as into the table maintenance code. Provides neat
features like automatic summary maintenance, for example.
• HBase has coprocessors
• Much more general framework that also subsumes triggers,
stored procedures, and cluster management hooks. (e.g
Access Control is an HBase coprocessor).
• Generality has its cost: very difficult to do some things that
are simple with iterators
• Some iterator use cases can be done with HBase filters
• I’ll call this one a tie
Copyright 2012 Cloudera Inc. All rights reserved 19
20. Differences – Web UI and Monitoring
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 20
21. Differences – Write-ahead logging
• HBase uses HDFS files as a WAL
• Takes advantage of HDFS performance improvements as they
are developed
• Same trusted replication and checksumming schemes as HDFS
• Accumulo has its own Logger implementation
• Extra daemons to run
• Does not leverage improvements in HDFS
• Won’t re-replicate if loggers go down
Winner:
Copyright 2012 Cloudera Inc. All rights reserved 21
22. Differences – Other features
• Accumulo has a nice mock Accumulo implementation
• Nice for testing user software
• Accumulo supports isolated scans on super-wide rows
• HBase supports wide rows but isolation properties are lost
• Accumulo supports tablet merging
• If tablets get too small, they’ll merge with neighbors
• Accumulo supports table snapshotting/cloning
• Other sundry features: logical clocks, RPC tracing, RPC
wire compatibility, and more.
Copyright 2012 Cloudera Inc. All rights reserved 22
23. Differences – Other features
• HBase has RPM and Debian packages as part of Apache
BigTop
• Integrated (and integration-tested) with Hive, Pig, and others
• HBase has commercial support available from Cloudera,
as well as several vendors and other projects building
on top (Lily, OMID, etc)
• HBase has first-class support for REST clients and thin
Thrift clients
• HBase has inter-cluster wide-area replication
• HBase has significantly more advanced bloom filters
and other such optimizations (thanks Facebook!)
Copyright 2012 Cloudera Inc. All rights reserved 23
24. Summary
• Neither system is better!
• One system may very well be better for your use case,
or for the community you want to interact with
• Over time, the feature sets are converging
• RFile vs HFile v2, Security, Caching, Compaction policies,
Iterators/Coprocessors
• Now that both projects are in Apache, open dialogue,
code sharing, and friendly competition will help make
both projects better!
Copyright 2012 Cloudera Inc. All rights reserved 24
25. Thanks!
Aaron Cordova and John Vines
(Accumulo committers) will now join
me for some discussion / questions
Email: todd@cloudera.com
Twitter: @tlipcon
Copyright 2012 Cloudera Inc. All rights reserved 25
Editor's Notes
Earlier, I said that Hbase is a big sorted map. Here is an example of a table. The map key is (row key+column+timestamp). The value is the cell contents. The rows in the map are sorted by key. In this example, Row1 has 3 columns in the "info" column family. Row2 only has a single column. A column can also be empty.Each row has a timestamp. By default, the timestamp is set to the current time (in milliseconds since the Unix Epoch, January 1st 1970) when the row is inserted. A client can specify a timestamp when inserting or retrieving data, and specify how many versions of each cell should be maintained.Data in HBase is non-typed; everything is an array of bytes. Rows are sorted lexicographically. This order is maintained on disk, so Row1 and Row2 can be read together in just one disk seek.
Given that Hbase stores a large sorted map, the API looks similar to a map. You can get or put individual rows, or scan a range of rows. There is also a very efficient way of incrementing a particular cell – this can be useful for maintaining high performance counters or statistics. Lastly, it’s possible to write MapReduce jobs that analyze the data in Hbase.
Earlier, I said that Hbase is a big sorted map. Here is an example of a table. The map key is (row key+column+timestamp). The value is the cell contents. The rows in the map are sorted by key. In this example, Row1 has 3 columns in the "info" column family. Row2 only has a single column. A column can also be empty.Each row has a timestamp. By default, the timestamp is set to the current time (in milliseconds since the Unix Epoch, January 1st 1970) when the row is inserted. A client can specify a timestamp when inserting or retrieving data, and specify how many versions of each cell should be maintained.Data in HBase is non-typed; everything is an array of bytes. Rows are sorted lexicographically. This order is maintained on disk, so Row1 and Row2 can be read together in just one disk seek.