The document discusses using HBase and R for fast time-series analytics of large datasets. It begins with an introduction to NoSQL and HBase, describing its key features. It then discusses a use case of analyzing sensor data from airport vehicles. The data model designs HBase to store the hierarchical time-series data. Hive and storing on HDFS directly are considered but rejected in favor of HBase. The presentation concludes with an overview of using the rhbase package to interface R with HBase.
Hadoop Summit 2015: Hive at Yahoo: Letters from the TrenchesMithun Radhakrishnan
Here's the talk that we presented at the Hadoop Summit 2015, in San Jose. This was an inside look at how we at Yahoo scaled Hive to work at Yahoo's data/metadata scale.
Apache HBase - Introduction & Use CasesData Con LA
HBase is an open source, distributed, column-oriented database modeled after Google's BigTable. It sits atop Hadoop, using HDFS for storage. HBase scales horizontally and supports fast random reads and writes. It is well-suited for large tables and high throughput access. Facebook uses HBase extensively for messaging and other applications due to its high write throughput and low latency reads. Other users include Flurry and Yahoo.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
Apache Drill is a scalable SQL query engine for analysis of large-scale datasets across various data sources like HDFS, HBase, Hive and others. It allows for ad-hoc analysis of datasets without requiring knowledge of the schema beforehand. Drill uses a distributed architecture with query coordinators and workers to process queries in parallel. It supports various interfaces like JDBC, ODBC and a web console for running SQL queries on different data sources.
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
Apache Drill and Zeppelin: Two Promising Tools You've Never Heard OfCharles Givre
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.
Hadoop Summit 2015: Hive at Yahoo: Letters from the TrenchesMithun Radhakrishnan
Here's the talk that we presented at the Hadoop Summit 2015, in San Jose. This was an inside look at how we at Yahoo scaled Hive to work at Yahoo's data/metadata scale.
Apache HBase - Introduction & Use CasesData Con LA
HBase is an open source, distributed, column-oriented database modeled after Google's BigTable. It sits atop Hadoop, using HDFS for storage. HBase scales horizontally and supports fast random reads and writes. It is well-suited for large tables and high throughput access. Facebook uses HBase extensively for messaging and other applications due to its high write throughput and low latency reads. Other users include Flurry and Yahoo.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
Apache Drill is a scalable SQL query engine for analysis of large-scale datasets across various data sources like HDFS, HBase, Hive and others. It allows for ad-hoc analysis of datasets without requiring knowledge of the schema beforehand. Drill uses a distributed architecture with query coordinators and workers to process queries in parallel. It supports various interfaces like JDBC, ODBC and a web console for running SQL queries on different data sources.
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
Apache Drill and Zeppelin: Two Promising Tools You've Never Heard OfCharles Givre
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Maintaining Low Latency While Maximizing Throughput on a Single ClusterMapR Technologies
The good news: Hadoop has a lot of tools. The bad news: Hadoop has a lot of tools, and conflicting priorities. This talk shows how advances in YARN and Mesos allow you to run multiple distinct workloads together. We show how to use SLA and latency rules along with preemption in YARN to maintain high throughput while guaranteeing latency for applications such as HBase and Drill
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
This is slides from our recent HadoopIsrael meetup. It is dedicated to comparison Spark and Tez frameworks.
In the end of the meetup there is small update about our ImpalaToGo project.
Apache Drill is the next generation of SQL query engines. It builds on ANSI SQL 2003, and extends it to handle new formats like JSON, Parquet, ORC, and the usual CSV, TSV, XML and other Hadoop formats. Most importantly, it melts away the barriers that have caused databases to become silos of data. It does so by able to handle schema-changes on the fly, enabling a whole new world of self-service and data agility never seen before.
2013 July 23 Toronto Hadoop User Group Hive TuningAdam Muise
This document provides an overview of Hive and its performance capabilities. It discusses Hive's SQL interface for querying large datasets stored in Hadoop, its architecture which compiles SQL queries into MapReduce jobs, and its support for SQL features. The document also covers techniques for optimizing Hive performance, including data abstractions like partitions, buckets and skews. It describes different join strategies in Hive like shuffle joins, broadcast joins and sort-merge bucket joins, and how shuffle joins are implemented in MapReduce.
This document discusses using Sqoop to transfer data between relational databases and Hadoop. It begins by providing context on big data and Hadoop. It then introduces Sqoop as a tool for efficiently importing and exporting large amounts of structured data between databases and Hadoop. The document explains that Sqoop allows importing data from databases into HDFS for analysis and exporting summarized data back to databases. It also outlines how Sqoop works, including providing a pluggable connector mechanism and allowing scheduling of jobs.
I gave this talk on the Highload++ conference 2015 in Moscow. Slides have been translated into English. They cover the Apache HAWQ components, its architecture, query processing logic, and also competitive information
This document provides an overview of using R, Hadoop, and Rhadoop for scalable analytics. It begins with introductions to basic R concepts like data types, vectors, lists, and data frames. It then covers Hadoop basics like MapReduce. Next, it discusses libraries for data manipulation in R like reshape2 and plyr. Finally, it focuses on Rhadoop projects like RMR for implementing MapReduce in R and considerations for using RMR effectively.
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
This document discusses Hive on Spark, which allows Apache Hive queries to run on Apache Spark. It provides background on Hive, Spark, and their limitations. Hive on Spark was developed by the Hive community to leverage Spark's more efficient execution while maintaining compatibility. Examples are given of how simple and join queries are translated from Hive operations to Spark transformations and actions. Improvements to Spark needed to better support Hive are also outlined. The author thanks contributors from various organizations working on Hive on Spark.
Apache Drill: Building Highly Flexible, High Performance Query Engines by M.C...The Hive
SQL is one of the most widely used languages to access, analyze, and manipulate structured data. As Hadoop gains traction within enterprise data architectures across industries, the need for SQL for both structured and loosely-structured data on Hadoop is growing rapidly Apache Drill started off with the audacious goal of delivering consistent, millisecond ANSI SQL query capability across wide range of data formats. At a high level, this translates to two key requirements – Schema Flexibility and Performance. This session will delve into the architectural details in delivering these two requirements and will share with the audience the nuances and pitfalls we ran into while developing Apache Drill.
NoSQL HBase schema design and SQL with Apache Drill Carol McDonald
The document provides an overview of HBase, including:
- HBase is a column-oriented NoSQL database modeled after Google's Bigtable. It is designed to handle large volumes of sparse data across clusters in a distributed fashion.
- Data in HBase is stored in tables containing rows, column families, columns, and versions. Tables are partitioned into regions distributed across region servers. The HMaster manages the cluster and Zookeeper coordinates operations.
- Common operations on HBase include put (insert/update), get, scan, and delete. The meta table stored in Zookeeper maps rows to their regions. This allows clients to efficiently access data in HBase's distributed architecture.
This document summarizes a presentation about using Apache Spark for various data analytics use cases. It discusses how Spark can be used for interactive SQL queries on large datasets, log file enrichment by connecting to data stores like HBase, mixing SQL and machine learning by accessing training and query engines in the same platform, and building recommendation engines by performing ETL, training models with MLlib, and serving recommendations with NoSQL. The presentation argues that Spark helps flatten the adoption curve by providing a unified framework for all these tasks.
The document provides an overview of the Apache Hadoop ecosystem. It describes Hadoop as a distributed, scalable storage and computation system based on Google's architecture. The ecosystem includes many related projects that interact, such as YARN, HDFS, Impala, Avro, Crunch, and HBase. These projects innovate independently but work together, with Hadoop serving as a flexible data platform at the core.
Ted Dunning presents information on Drill and Spark SQL. Drill is a query engine that operates on batches of rows in a pipelined and optimistic manner, while Spark SQL provides SQL capabilities on top of Spark's RDD abstraction. The document discusses the key differences in their approaches to optimization, execution, and security. It also explores opportunities for unification by allowing Drill and Spark to work together on the same data.
This document provides advice for starting a Big Data project. It recommends defining a clear business goal for the project first before collecting data. Consider different types and sources of data beyond just internal company data. Get management buy-in to ensure results are acknowledged and acted on. Address privacy and security issues early on. Start with a small, well-scoped project rather than trying to do too much initially. The key is reaching your business goal, not the size of the data or tools used.
The document summarizes a Think Big Bootcamp project involving the ingestion and preliminary analysis of aircraft registry data from the FAA. It describes how the data was ingested using Python and Hadoop, then loaded into Hive tables. Initial exploration found the most frequently reported crafts and analyzed acceptance rates. Site comparison showed differences in average speed and altitude between two sites. Master data queries were created to summarize models, aircraft, and owners. Data visualizations analyzed fastest planes, speed vs altitude by make, unique flights by airline, and number of sightings by aircraft make.
Jim Scott, CHUG co-founder and Director, Enterprise Strategy and Architecture for MapR presents "Using Apache Drill". This presentation was given on August 13th, 2014 at the Nokia office in Chicago, IL.
Jim has held positions running Operations, Engineering, Architecture and QA teams. He has worked in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. His work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.
Apache Drill brings the power of standard ANSI:SQL 2003 to your desktop and your clusters. It is like AWK for Hadoop. Drill supports querying schemaless systems like HBase, Cassandra and MongoDB. Use standard JDBC and ODBC APIs to use Drill from your custom applications. Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. This presentation contains live demonstrations.
The video can be found here: http://vimeo.com/chug/using-apache-drill
Maintaining Low Latency While Maximizing Throughput on a Single ClusterMapR Technologies
The good news: Hadoop has a lot of tools. The bad news: Hadoop has a lot of tools, and conflicting priorities. This talk shows how advances in YARN and Mesos allow you to run multiple distinct workloads together. We show how to use SLA and latency rules along with preemption in YARN to maintain high throughput while guaranteeing latency for applications such as HBase and Drill
This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include:
- Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries.
- Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering.
- The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans.
- Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
This is slides from our recent HadoopIsrael meetup. It is dedicated to comparison Spark and Tez frameworks.
In the end of the meetup there is small update about our ImpalaToGo project.
Apache Drill is the next generation of SQL query engines. It builds on ANSI SQL 2003, and extends it to handle new formats like JSON, Parquet, ORC, and the usual CSV, TSV, XML and other Hadoop formats. Most importantly, it melts away the barriers that have caused databases to become silos of data. It does so by able to handle schema-changes on the fly, enabling a whole new world of self-service and data agility never seen before.
2013 July 23 Toronto Hadoop User Group Hive TuningAdam Muise
This document provides an overview of Hive and its performance capabilities. It discusses Hive's SQL interface for querying large datasets stored in Hadoop, its architecture which compiles SQL queries into MapReduce jobs, and its support for SQL features. The document also covers techniques for optimizing Hive performance, including data abstractions like partitions, buckets and skews. It describes different join strategies in Hive like shuffle joins, broadcast joins and sort-merge bucket joins, and how shuffle joins are implemented in MapReduce.
This document discusses using Sqoop to transfer data between relational databases and Hadoop. It begins by providing context on big data and Hadoop. It then introduces Sqoop as a tool for efficiently importing and exporting large amounts of structured data between databases and Hadoop. The document explains that Sqoop allows importing data from databases into HDFS for analysis and exporting summarized data back to databases. It also outlines how Sqoop works, including providing a pluggable connector mechanism and allowing scheduling of jobs.
I gave this talk on the Highload++ conference 2015 in Moscow. Slides have been translated into English. They cover the Apache HAWQ components, its architecture, query processing logic, and also competitive information
This document provides an overview of using R, Hadoop, and Rhadoop for scalable analytics. It begins with introductions to basic R concepts like data types, vectors, lists, and data frames. It then covers Hadoop basics like MapReduce. Next, it discusses libraries for data manipulation in R like reshape2 and plyr. Finally, it focuses on Rhadoop projects like RMR for implementing MapReduce in R and considerations for using RMR effectively.
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
This document discusses Hive on Spark, which allows Apache Hive queries to run on Apache Spark. It provides background on Hive, Spark, and their limitations. Hive on Spark was developed by the Hive community to leverage Spark's more efficient execution while maintaining compatibility. Examples are given of how simple and join queries are translated from Hive operations to Spark transformations and actions. Improvements to Spark needed to better support Hive are also outlined. The author thanks contributors from various organizations working on Hive on Spark.
Apache Drill: Building Highly Flexible, High Performance Query Engines by M.C...The Hive
SQL is one of the most widely used languages to access, analyze, and manipulate structured data. As Hadoop gains traction within enterprise data architectures across industries, the need for SQL for both structured and loosely-structured data on Hadoop is growing rapidly Apache Drill started off with the audacious goal of delivering consistent, millisecond ANSI SQL query capability across wide range of data formats. At a high level, this translates to two key requirements – Schema Flexibility and Performance. This session will delve into the architectural details in delivering these two requirements and will share with the audience the nuances and pitfalls we ran into while developing Apache Drill.
NoSQL HBase schema design and SQL with Apache Drill Carol McDonald
The document provides an overview of HBase, including:
- HBase is a column-oriented NoSQL database modeled after Google's Bigtable. It is designed to handle large volumes of sparse data across clusters in a distributed fashion.
- Data in HBase is stored in tables containing rows, column families, columns, and versions. Tables are partitioned into regions distributed across region servers. The HMaster manages the cluster and Zookeeper coordinates operations.
- Common operations on HBase include put (insert/update), get, scan, and delete. The meta table stored in Zookeeper maps rows to their regions. This allows clients to efficiently access data in HBase's distributed architecture.
This document summarizes a presentation about using Apache Spark for various data analytics use cases. It discusses how Spark can be used for interactive SQL queries on large datasets, log file enrichment by connecting to data stores like HBase, mixing SQL and machine learning by accessing training and query engines in the same platform, and building recommendation engines by performing ETL, training models with MLlib, and serving recommendations with NoSQL. The presentation argues that Spark helps flatten the adoption curve by providing a unified framework for all these tasks.
The document provides an overview of the Apache Hadoop ecosystem. It describes Hadoop as a distributed, scalable storage and computation system based on Google's architecture. The ecosystem includes many related projects that interact, such as YARN, HDFS, Impala, Avro, Crunch, and HBase. These projects innovate independently but work together, with Hadoop serving as a flexible data platform at the core.
Ted Dunning presents information on Drill and Spark SQL. Drill is a query engine that operates on batches of rows in a pipelined and optimistic manner, while Spark SQL provides SQL capabilities on top of Spark's RDD abstraction. The document discusses the key differences in their approaches to optimization, execution, and security. It also explores opportunities for unification by allowing Drill and Spark to work together on the same data.
This document provides advice for starting a Big Data project. It recommends defining a clear business goal for the project first before collecting data. Consider different types and sources of data beyond just internal company data. Get management buy-in to ensure results are acknowledged and acted on. Address privacy and security issues early on. Start with a small, well-scoped project rather than trying to do too much initially. The key is reaching your business goal, not the size of the data or tools used.
The document summarizes a Think Big Bootcamp project involving the ingestion and preliminary analysis of aircraft registry data from the FAA. It describes how the data was ingested using Python and Hadoop, then loaded into Hive tables. Initial exploration found the most frequently reported crafts and analyzed acceptance rates. Site comparison showed differences in average speed and altitude between two sites. Master data queries were created to summarize models, aircraft, and owners. Data visualizations analyzed fastest planes, speed vs altitude by make, unique flights by airline, and number of sightings by aircraft make.
1) The document discusses problems with the current public transportation system including inconvenient routes that require transfers, lack of on-time arrival information for buses, and overcrowded buses.
2) It proposes using a big data system to capture customer travel patterns automatically and make route and service decisions based on how customers actually use public transportation rather than just their opinions.
3) This big data system would provide high quality predictive and real-time information to customers about bus arrival times and occupancy to improve the user experience and attract more customers to public transportation.
CitySprint Fleetmapper use case -Big Data BootcampEduard Lazar
This document discusses geospatial and time series analyses performed on data from CitySprint's fleet to optimize resource allocation and identify areas for potential expansion. K-means clustering was used to identify 40 and 100 pickup location clusters to validate service centers and find expansion opportunities. Heat maps showed pickup location density and demand variation over time to position couriers in hot spots. The solution used Spark and ArcGIS for scalable, in-memory processing and visualization to analyze large datasets from CitySprint's data warehouse.
Atlanta meetup presentation, discussion around big data processing engines (Hive, HBase, Druid, Spark). Weighs the relative strengths of each engine and which use cases each of the engines are most suited for
The document summarizes a presentation about Apache Ratis, a Raft consensus library. It introduces Raft consensus and describes Ratis' features like leader election, log replication, pluggable components, and use cases in Hadoop projects like Ozone. It also outlines Ratis' development status and future work areas like performance, metrics, security, and documentation.
The document discusses new features and enhancements in Apache Hive 3.0 including:
1. Improved transactional capabilities with ACID v2 that provide faster performance compared to previous versions while also supporting non-bucketed tables and non-ORC formats.
2. New materialized view functionality that allows queries to be rewritten to improve performance by leveraging pre-computed results stored in materialized views.
3. Enhancements to LLAP workload management that improve query scheduling and enable better sharing of resources across users.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer
This document discusses SQL Server and big data analytics projects in the real world. It covers the big data technology landscape, big data analytics, and three big data analytics scenarios using different technologies like Hadoop, MongoDB, and SQL Server. It also discusses SQL Server's role in the big data world and how to get data into Hadoop for analysis.
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Hadoop and NoSQL joining forces by Dale Kim of MapRData Con LA
More and more organizations are turning to Hadoop and NoSQL to manage big data. In fact, many IT professionals consider each of those terms to be synonymous with big data. At the same time, these two technologies are seen as different beasts that handle different challenges. That means they are often deployed in a rather disjointed way, even when intended to solve the same overarching business problem. The emerging trend of “in-Hadoop databases” promises to narrow the deployment gap between them and enable new enterprise applications. In this talk, Dale will describe that integrated architecture and how customers have deployed it to benefit both the technical and the business teams.
From: DataWorks Summit Munich 2017 - 20170406
While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.
Webinar: Selecting the Right SQL-on-Hadoop SolutionMapR Technologies
In the crowded SQL-on-Hadoop market, choosing the right solution for your business can be difficult. In this webinar, learn firsthand from Rick van der Lans, independent analyst and managing director of R20/Consultancy, how to sort through this market complexity and what tough questions to ask when evaluating perspective SQL-on-Hadoop solutions.
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)VMware Tanzu
Recorded at SpringOne2GX 2013 in Santa Clara, CA
Speaker: Adam Shook
This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more.
Learn More about Spring XD at: http://projects.spring.io/spring-xd
Learn More about Gemfire XD at:
http://www.gopivotal.com/big-data/pivotal-hd
Technologies for Data Analytics PlatformN Masahiro
This document discusses building a data analytics platform and summarizes various technologies that can be used. It begins by outlining reasons for analyzing data like reporting, monitoring, and exploratory analysis. It then discusses using relational databases, parallel databases, Hadoop, and columnar storage to store and process large volumes of data. Streaming technologies like Storm, Kafka, and services like Redshift, BigQuery, and Treasure Data are also summarized as options for a complete analytics platform.
Predictive Analytics and Machine Learning…with SAS and Apache HadoopHortonworks
In this interactive webinar, we'll walk through use cases on how you can use advanced analytics like SAS Visual Statistics and In-Memory Statistic with Hortonworks’ data platform (HDP) to reveal insights in your big data and redefine how your organization solves complex problems.
This document discusses cloud and big data technologies. It provides an overview of Hadoop and its ecosystem, which includes components like HDFS, MapReduce, HBase, Zookeeper, Pig and Hive. It also describes how data is stored in HDFS and HBase, and how MapReduce can be used for parallel processing across large datasets. Finally, it gives examples of using MapReduce to implement algorithms for word counting, building inverted indexes and performing joins.
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.
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
Cómo Oracle ha logrado separar el motor SQL de su emblemática base de datos para procesar las consultas y los drivers de acceso que permiten leer datos, tanto de ficheros sobre el Hadoop Distributed File System, como de la herramienta de Data Warehousing, HIVE.
This document discusses the Stinger initiative to improve the performance of Apache Hive. Stinger aims to speed up Hive queries by 100x, scale queries from terabytes to petabytes of data, and expand SQL support. Key developments include optimizing Hive to run on Apache Tez, the vectorized query execution engine, cost-based optimization using Optiq, and performance improvements from the ORC file format. The goals of Stinger Phase 3 are to deliver interactive query performance for Hive by integrating these technologies.