This presentation is based on my experience while learning HIVE. Most of the things(Limitation and features) covered in ppt are in incubating phase while writing this tutorial.
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Hive Training -- Motivations and Real World Use Casesnzhang
Hive is an open source data warehouse systems based on Hadoop, a MapReduce implementation.
This presentation introduces the motivations of developing Hive and how Hive is used in the real world situation, particularly in Facebook.
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Hive Training -- Motivations and Real World Use Casesnzhang
Hive is an open source data warehouse systems based on Hadoop, a MapReduce implementation.
This presentation introduces the motivations of developing Hive and how Hive is used in the real world situation, particularly in Facebook.
Apache Hive and HBase are very popular projects in the Hadoop ecosystem. Using Hive with HBase was made possible by contributions from Facebook around 2010. In this talk, we will go over the details of how the integration works, and talk about recent improvements. Specifically, we will cover the basic architecture, schema and data type mappings, and recent filter pushdown optimizations. We will also go into detail about the security aspects of Hadoop/HBase related to Hive setups.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Introduction to Hive and HCatalog presentation by Mark Grover at NYC HUG. A video of this presentation is available at https://www.youtube.com/watch?v=JGwhfr4qw5s
Learning Objectives - This module will help you in understanding Apache Hive Installation, Loading and Querying Data in Hive and so on.
Topics - Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables (Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
Apache Hive and HBase are very popular projects in the Hadoop ecosystem. Using Hive with HBase was made possible by contributions from Facebook around 2010. In this talk, we will go over the details of how the integration works, and talk about recent improvements. Specifically, we will cover the basic architecture, schema and data type mappings, and recent filter pushdown optimizations. We will also go into detail about the security aspects of Hadoop/HBase related to Hive setups.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Introduction to Hive and HCatalog presentation by Mark Grover at NYC HUG. A video of this presentation is available at https://www.youtube.com/watch?v=JGwhfr4qw5s
Learning Objectives - This module will help you in understanding Apache Hive Installation, Loading and Querying Data in Hive and so on.
Topics - Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables (Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
Hadoop Summit 2012 | A New Generation of Data Transfer Tools for Hadoop: Sqoop 2Cloudera, Inc.
Apache Sqoop (incubating) was created to efficiently transfer big data between Hadoop related systems (such as HDFS, Hive, and HBase) and structured data stores (such as relational databases, data warehouses, and NoSQL systems). The popularity of Sqoop in enterprise systems confirms that Sqoop does bulk transfer admirably. In the meantime, we have encountered many new challenges that have outgrown the abilities of the current infrastructure. To fulfill more data integration use cases as well as become easier to manage and operate, a new generation of Sqoop, also known as Sqoop 2, is currently undergoing development to address several key areas, including ease of use, ease of extension, and security. This session will talk about Sqoop 2 from both the development and operations perspectives.
Data Engineering with Spring, Hadoop and Hive Alex Silva
This presentation will outline the evolution of the monitoring data platform pipeline at Rackspace and explore the compute and data management challenges we have faced at this scale. We will focus on our use of Hadoop and Hive as data storage and transformation platforms while discussing the technology stack, key architectural decisions, observations and pitfalls encountered in building the pipeline.
Cost-based query optimization in Apache HiveJulian Hyde
Tez is making Hive faster, and now cost-based optimization (CBO) is making it smarter. A new initiative in Hive 0.13 introduces cost-based optimization for the first time, based on the Optiq framework.
Optiq’s lead developer Julian Hyde shows the improvements that CBO is bringing to Hive 0.13. For those interested in Hive internals, he gives an overview of the Optiq framework and shows some of the improvements that are coming to future versions of Hive.
Lightning Talk: Why and How to Integrate MongoDB and NoSQL into Hadoop Big Da...MongoDB
Drawn from Think Big's experience on real-world client projects, Think Big Academy Director and Principal Architect Jeffrey Breen will review specific ways to integrate NoSQL databases into Hadoop-based Big Data systems: preserving state in otherwise stateless processes; storing pre-computed metrics and aggregates to enable interactive analytics and reporting; and building a secondary index to provide low latency, random access to data stored stored on the high latency HDFS. A working example of secondary indexing is presented in which MongoDB is used to index web site visitor locations from Omniture clickstream data stored on HDFS.
Lightning Talk: Why and How to Integrate MongoDB and NoSQL into Hadoop Big Da...MongoDB
Drawn from Think Big's experience on real-world client projects, Think Big Academy Director and Principal Architect Jeffrey Breen will review specific ways to integrate NoSQL databases into Hadoop-based Big Data systems: preserving state in otherwise stateless processes; storing pre-computed metrics and aggregates to enable interactive analytics and reporting; and building a secondary index to provide low latency, random access to data stored stored on the high latency HDFS. A working example of secondary indexing is presented in which MongoDB is used to index web site visitor locations from Omniture clickstream data stored on HDFS.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
IBM THINK 2019 - Self-Service Cloud Data Management with SQL Torsten Steinbach
SQL is a powerful language to express data transformations. But did you know that you can also use IBM Cloud SQL to convert data between various data formats and layouts on disks? In this session, you will see the full power of using SQL Query to move and transform your cloud data in an entirely self-service fashion. You can specify any data format, layout or partitioning with a simple SQL statement. See how you can move and transform terabytes of data in the cloud in a very scalable fashion and still being charged only for the individual SQL movement and transformation jobs without having standing costs.
Practical Partitioning in Production with PostgresEDB
Has your table become too large to handle? Have you thought about chopping it up into smaller pieces that are easier to query and maintain? What if it's in constant use? An introduction to the problems that can arise and how PostgreSQL's partitioning features can help, followed by a real-world scenario of partitioning an existing huge table on a live system. We will be looking at the problems caused by having very large tables in your database and how declarative table partitioning in Postgres can help. Also, how to perform dimensioning before but also after creating huge tables, partitioning key selection, the importance of upgrading to get the latest Postgres features and finally we will dive into a real-world scenario of having to partition an existing huge table in use on a production system.
Join Postgres experts Bruce Momjian, Rushabh Lathia, and Marc Linster as they preview everything new in PostgreSQL 13. You don’t want to miss this!
This session will explore:
- Performance benchmarking: 4 years of going faster
- Logical subscription for partitioned tables
- Partitionwise joins
- BEFORE row-level triggers
- Parallel vacuum for indexes
- Corruption checking: pg_catcheck
- Improved security: libpq with channel binding
- EDB Postgres Advanced Server improved partitioning features
- Additional enhancements - PostgreSQL and EDB Postgres - -
- Advanced Server
Want to get all the PostgreSQL 13 updates but can’t make the live webinar? Register here and we will send you the slides and recording following the session.
Practical Partitioning in Production with PostgresJimmy Angelakos
Has your table become too large to handle? Have you thought about chopping it up into smaller pieces that are easier to query and maintain? What if it's in constant use?
An introduction to the problems that can arise and how PostgreSQL's partitioning features can help, followed by a real-world scenario of partitioning an existing huge table on a live system.
Talk from Postgres Vision 2021
This white paper explains how JethroData can help you achieve truly interactive response times for BI on big data, and how the underlying technology works.
It analyzes the challenges of implementing indexes for big data and how JethroData solved these challenges. It then discusses how the JethroData design of separating compute from storage works with Hadoop and with Amazon S3. Finally, it briefly discusses some of the main features behind JethroData's performance, including I/O, query planning and execution features.
PostgreSQL 13 is coming. Find out how to harness the power of new & improved features in PostgreSQL 13.
This presentation explores the following:
- Performance benchmarking: 4 years of going faster
- Logical subscription for partitioned tables
- Partitionwise joins
- BEFORE row-level triggers
- Parallel vacuum for indexes
- Corruption checking: pg_catcheck
- Improved security: libpq with channel binding
- EDB Postgres Advanced Server improved partitioning features
- Additional enhancements - PostgreSQL and EDB Postgres Advanced Server
It’s no longer a world of just relational databases. Companies are increasingly adopting specialized datastores such as Hadoop, HBase, MongoDB, Elasticsearch, Solr and S3. Apache Drill, an open source, in-memory, columnar SQL execution engine, enables interactive SQL queries against more datastores.
Altinity Quickstart for ClickHouse-2202-09-15.pdfAltinity Ltd
Welcome to a live session of our popular introduction to ClickHouse application development. The talk explains what ClickHouse is and how to install it. We then work through the basics of inserting and selecting data, followed by tips on how to maximize the legendary performance of ClickHouse. You’ll get everything you need to get started on your own application, including some time at the end for questions.
This is the Day-4 lab exercise for CGI group webinar series. It primarily includes demonstrations on Hive, Analytics and other tools on the Cloudera Hadoop Platform.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
3. | 2012 Cognizant Technology Solutions
Hive Provides
3
• Ability to bring structure to various data Formats
• Simple interface for ad hoc querying,analyzing and
summarizing large amounts of data
• Access to files on various data stores such
as HDFS and HBase