PXF is a unified access framework that provides a uniform SQL interface for heterogeneous data sources on HDFS. It exploits parallelism to efficiently access data across various storage formats and data sources. PXF uses a pluggable architecture with built-in connectors that allow it to access data in HDFS files, Hive tables, HBase tables, and other data sources. It provides a common developer view and allows writing queries against external data using various profile definitions and plugins.
1. HCatalog is a table and storage management layer for Hadoop that provides a relational view of data in HDFS and abstracts data formats and locations from users.
2. Previously, HAWQ accessed Hive tables through PXF using external tables, but this required specifying the schema, location, and format which was error prone and wouldn't detect metadata changes.
3. The new integration retrieves metadata from HCatalog and parses it into in-memory catalog tables to provide dynamic access to Hive tables from HAWQ without needing to specify schemas.
Hypertable is an open source Bigtable clone that manages massive sparse tables with timestamped cell versions using a single primary key index. It is used by companies like Zvents and Baidu to process large amounts of data at scales of billions of cells per day and petabytes of data. Hypertable scales horizontally on commodity hardware and provides high performance through techniques like block caching, bloom filters, and access group optimizations. It is written in C++ for efficiency and provides client APIs in multiple languages.
Hypertable is an open source, massively scalable database modeled after Google's Bigtable. It is written in C++ for high performance and supports Apache Thrift interfaces for popular languages. Hypertable is actively developed, has over 8 years of development, and supports features like namespaces, atomic counters, secondary indexes, regex filtering, and Hadoop integration. It is designed for horizontal scalability and sparse data structures, allowing for high throughput on both reads and writes even with large datasets.
This presentation covers some common terminology used to describe NoSQL databases, goes into depth on some popular scalable database architectures, and includes an overview of Hypertable
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
The document provides an introduction to Hadoop and big data concepts. It discusses key topics like what big data is characterized by the three V's of volume, velocity and variety. It then defines Hadoop as a framework for distributed storage and processing of large datasets using commodity hardware. The rest of the document outlines the main components of the Hadoop ecosystem including HDFS, YARN, MapReduce, Hive, Pig, Zookeeper, Flume and Sqoop and provides brief descriptions of each.
Session 03 - Hadoop Installation and Basic CommandsAnandMHadoop
In this session you will learn:
Hadoop Installation and Commands
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
PXF is a unified access framework that provides a uniform SQL interface for heterogeneous data sources on HDFS. It exploits parallelism to efficiently access data across various storage formats and data sources. PXF uses a pluggable architecture with built-in connectors that allow it to access data in HDFS files, Hive tables, HBase tables, and other data sources. It provides a common developer view and allows writing queries against external data using various profile definitions and plugins.
1. HCatalog is a table and storage management layer for Hadoop that provides a relational view of data in HDFS and abstracts data formats and locations from users.
2. Previously, HAWQ accessed Hive tables through PXF using external tables, but this required specifying the schema, location, and format which was error prone and wouldn't detect metadata changes.
3. The new integration retrieves metadata from HCatalog and parses it into in-memory catalog tables to provide dynamic access to Hive tables from HAWQ without needing to specify schemas.
Hypertable is an open source Bigtable clone that manages massive sparse tables with timestamped cell versions using a single primary key index. It is used by companies like Zvents and Baidu to process large amounts of data at scales of billions of cells per day and petabytes of data. Hypertable scales horizontally on commodity hardware and provides high performance through techniques like block caching, bloom filters, and access group optimizations. It is written in C++ for efficiency and provides client APIs in multiple languages.
Hypertable is an open source, massively scalable database modeled after Google's Bigtable. It is written in C++ for high performance and supports Apache Thrift interfaces for popular languages. Hypertable is actively developed, has over 8 years of development, and supports features like namespaces, atomic counters, secondary indexes, regex filtering, and Hadoop integration. It is designed for horizontal scalability and sparse data structures, allowing for high throughput on both reads and writes even with large datasets.
This presentation covers some common terminology used to describe NoSQL databases, goes into depth on some popular scalable database architectures, and includes an overview of Hypertable
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
The document provides an introduction to Hadoop and big data concepts. It discusses key topics like what big data is characterized by the three V's of volume, velocity and variety. It then defines Hadoop as a framework for distributed storage and processing of large datasets using commodity hardware. The rest of the document outlines the main components of the Hadoop ecosystem including HDFS, YARN, MapReduce, Hive, Pig, Zookeeper, Flume and Sqoop and provides brief descriptions of each.
Session 03 - Hadoop Installation and Basic CommandsAnandMHadoop
In this session you will learn:
Hadoop Installation and Commands
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core of Hadoop includes HDFS for distributed storage, and MapReduce for distributed processing. Other Hadoop projects include Pig for data flows, ZooKeeper for coordination, and YARN for job scheduling. Key Hadoop daemons include the NameNode, Secondary NameNode, DataNodes, JobTracker and TaskTrackers.
Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.
Hadoop Performance Optimization at Scale, Lessons Learned at TwitterDataWorks Summit
- Profiling Hadoop jobs at Twitter revealed that compression/decompression of intermediate data and deserialization of complex object keys were very expensive. Optimizing these led to performance improvements of 1.5x or more.
- Using columnar file formats like Apache Parquet allows reading only needed columns, avoiding deserialization of unused data. This led to gains of up to 3x.
- Scala macros were developed to generate optimized implementations of Hadoop's RawComparator for common data types, avoiding deserialization for sorting.
HCatalog provides table management capabilities for Hadoop that allow different tools like Pig and Hive to share data without needing to know the internal data formats or needing custom loaders and serializers. It manages the data formats and schema, and allows new columns and storage format changes without changing existing data. HCatalog uses the underlying storage system's permissions for security and supports HDFS, HBase, and other data sources. It is an Apache incubator project currently working on features like support for additional data sources, improved integration with Hive, and a web services interface.
In-core compression: how to shrink your database size in several timesAleksander Alekseev
The document discusses techniques for compressing database size in Postgres, including:
1. Using in-core block-level compression as a feature of Postgres Pro EE to shrink database size by several times.
2. The ZSON extension provides transparent JSONB compression by replacing common strings with 16-bit codes and compressing the data.
3. Various schema optimizations like proper data types, column ordering, and packing data can reduce size by improving storage layout and enabling TOAST compression.
Hadoop Cluster Configuration and Data Loading - Module 2Rohit Agrawal
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
CopyTable allows copying data between HBase tables either within or between clusters. Export dumps the contents of a table to HDFS in sequence files. Import loads exported data back into HBase. For regular incremental backups, Export is recommended with a hierarchical output directory structure organized by date/time. Data can then be restored using Import on demand. Backup/restore should be done during off-peak hours to reduce overhead.
Spark is a fast and general cluster computing system that improves on MapReduce by keeping data in-memory between jobs. It was developed in 2009 at UC Berkeley and open sourced in 2010. Spark core provides in-memory computing capabilities and a programming model that allows users to write programs as transformations on distributed datasets.
The document provides descriptions of various components in Hadoop including Hadoop Core, Pig, ZooKeeper, JobTracker, TaskTracker, NameNode, Secondary NameNode, and the design of HDFS. It also discusses how to deploy Hadoop in a distributed environment and configure core-site.xml, hdfs-site.xml, and mapred-site.xml.
GlusterFS uses "translators" to modify and route file requests between users and storage bricks. Translators can convert request types, modify request properties like paths or flags, intercept or block requests, and spawn new requests. This allows GlusterFS to provide features like replication, caching, and integration with other systems, but also enables custom file systems to be built by modifying the translators. The asynchronous programming model and shared context objects allow translators to cooperate complex workflows across multiple servers.
Hypertable is an open source, scalable database modeled after Google's Bigtable database. It provides high performance for massive sparse tables of information using a single primary key index. Key features include auto-sharding of data, support for popular programming languages through a Thrift interface, and deployment at companies using architectures like MongoDB, Cassandra, and Dynamo. It uses techniques like consistent hashing, order-preserving partitioning, and LSM trees to optimize performance.
This document provides an overview and outline of topics related to advanced features in HDF5, including:
- HDF5 supports various datatypes like atomic, compound, array, and variable-length datatypes. It allows creation of complex user-defined datatypes.
- Partial I/O in HDF5 allows reading and writing subsets of datasets using hyperslab selections, which describe subsets through properties like start point, stride, count, and block size.
- Chunking and compression can be used to improve performance and reduce storage needs when working with subsets of large HDF5 datasets.
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The presentation contents a fundamental knowlege about Hadoop Ecosystem. It includes a popular technology as HDFS, YARN, HIVE Spark and Flink
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase Cloudera, Inc.
The document discusses Honeycomb, an open-source MySQL storage engine backed by HBase. Honeycomb allows MySQL to use HBase for storage, enabling features such as automatic sharding, replication, and MapReduce integration. The document outlines Honeycomb's schema design in HBase and how data is stored by row and index format without duplication. Performance tests show Honeycomb can achieve 51-75% of the scan speed of raw HBase.
This document provides an overview of Drupal 7's Database API. It describes the database connection and query classes, including SelectQuery, DeleteQuery, UpdateQuery, and InsertQuery. It also covers logging queries with DatabaseLog, running transactions with db_transaction(), and handling errors. Links are provided for additional documentation on the Drupal database API.
The document provides an overview of a training course on advanced file processing and system automation using Perl. It covers topics like opening and reading files in different modes, file locking, reading directories and files recursively, and interacting with the operating system. The document also lists modules to be covered including file processing, system interaction, network management, and Perl coding guidelines.
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 is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core of Hadoop includes HDFS for distributed storage, and MapReduce for distributed processing. Other Hadoop projects include Pig for data flows, ZooKeeper for coordination, and YARN for job scheduling. Key Hadoop daemons include the NameNode, Secondary NameNode, DataNodes, JobTracker and TaskTrackers.
Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.
Hadoop Performance Optimization at Scale, Lessons Learned at TwitterDataWorks Summit
- Profiling Hadoop jobs at Twitter revealed that compression/decompression of intermediate data and deserialization of complex object keys were very expensive. Optimizing these led to performance improvements of 1.5x or more.
- Using columnar file formats like Apache Parquet allows reading only needed columns, avoiding deserialization of unused data. This led to gains of up to 3x.
- Scala macros were developed to generate optimized implementations of Hadoop's RawComparator for common data types, avoiding deserialization for sorting.
HCatalog provides table management capabilities for Hadoop that allow different tools like Pig and Hive to share data without needing to know the internal data formats or needing custom loaders and serializers. It manages the data formats and schema, and allows new columns and storage format changes without changing existing data. HCatalog uses the underlying storage system's permissions for security and supports HDFS, HBase, and other data sources. It is an Apache incubator project currently working on features like support for additional data sources, improved integration with Hive, and a web services interface.
In-core compression: how to shrink your database size in several timesAleksander Alekseev
The document discusses techniques for compressing database size in Postgres, including:
1. Using in-core block-level compression as a feature of Postgres Pro EE to shrink database size by several times.
2. The ZSON extension provides transparent JSONB compression by replacing common strings with 16-bit codes and compressing the data.
3. Various schema optimizations like proper data types, column ordering, and packing data can reduce size by improving storage layout and enabling TOAST compression.
Hadoop Cluster Configuration and Data Loading - Module 2Rohit Agrawal
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
CopyTable allows copying data between HBase tables either within or between clusters. Export dumps the contents of a table to HDFS in sequence files. Import loads exported data back into HBase. For regular incremental backups, Export is recommended with a hierarchical output directory structure organized by date/time. Data can then be restored using Import on demand. Backup/restore should be done during off-peak hours to reduce overhead.
Spark is a fast and general cluster computing system that improves on MapReduce by keeping data in-memory between jobs. It was developed in 2009 at UC Berkeley and open sourced in 2010. Spark core provides in-memory computing capabilities and a programming model that allows users to write programs as transformations on distributed datasets.
The document provides descriptions of various components in Hadoop including Hadoop Core, Pig, ZooKeeper, JobTracker, TaskTracker, NameNode, Secondary NameNode, and the design of HDFS. It also discusses how to deploy Hadoop in a distributed environment and configure core-site.xml, hdfs-site.xml, and mapred-site.xml.
GlusterFS uses "translators" to modify and route file requests between users and storage bricks. Translators can convert request types, modify request properties like paths or flags, intercept or block requests, and spawn new requests. This allows GlusterFS to provide features like replication, caching, and integration with other systems, but also enables custom file systems to be built by modifying the translators. The asynchronous programming model and shared context objects allow translators to cooperate complex workflows across multiple servers.
Hypertable is an open source, scalable database modeled after Google's Bigtable database. It provides high performance for massive sparse tables of information using a single primary key index. Key features include auto-sharding of data, support for popular programming languages through a Thrift interface, and deployment at companies using architectures like MongoDB, Cassandra, and Dynamo. It uses techniques like consistent hashing, order-preserving partitioning, and LSM trees to optimize performance.
This document provides an overview and outline of topics related to advanced features in HDF5, including:
- HDF5 supports various datatypes like atomic, compound, array, and variable-length datatypes. It allows creation of complex user-defined datatypes.
- Partial I/O in HDF5 allows reading and writing subsets of datasets using hyperslab selections, which describe subsets through properties like start point, stride, count, and block size.
- Chunking and compression can be used to improve performance and reduce storage needs when working with subsets of large HDF5 datasets.
Spencer Christensen
There are many aspects to managing an RDBMS. Some of these are handled by an experienced DBA, but there are a good many things that any sys admin should be able to take care of if they know what to look for.
This presentation will cover basics of managing Postgres, including creating database clusters, overview of configuration, and logging. We will also look at tools to help monitor Postgres and keep an eye on what is going on. Some of the tools we will review are:
* pgtop
* pg_top
* pgfouine
* check_postgres.pl.
Check_postgres.pl is a great tool that can plug into your Nagios or Cacti monitoring systems, giving you even better visibility into your databases.
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The presentation contents a fundamental knowlege about Hadoop Ecosystem. It includes a popular technology as HDFS, YARN, HIVE Spark and Flink
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase Cloudera, Inc.
The document discusses Honeycomb, an open-source MySQL storage engine backed by HBase. Honeycomb allows MySQL to use HBase for storage, enabling features such as automatic sharding, replication, and MapReduce integration. The document outlines Honeycomb's schema design in HBase and how data is stored by row and index format without duplication. Performance tests show Honeycomb can achieve 51-75% of the scan speed of raw HBase.
This document provides an overview of Drupal 7's Database API. It describes the database connection and query classes, including SelectQuery, DeleteQuery, UpdateQuery, and InsertQuery. It also covers logging queries with DatabaseLog, running transactions with db_transaction(), and handling errors. Links are provided for additional documentation on the Drupal database API.
The document provides an overview of a training course on advanced file processing and system automation using Perl. It covers topics like opening and reading files in different modes, file locking, reading directories and files recursively, and interacting with the operating system. The document also lists modules to be covered including file processing, system interaction, network management, and Perl coding guidelines.
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 is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you
This certificate of appreciation was presented to Shivram Mani from the Apache Software Foundation for serving as a mentor during Google Summer of Code 2016 from April 22 to August 23, 2016. Jason Titus, VP of Engineering, recognized Shivram Mani's contributions as a mentor during the summer program.
This document summarizes a presentation about managing Apache HAWQ, an open source massively parallel processing (MPP) database, using Apache Ambari. It discusses how Ambari integrates with HAWQ for installation, configuration, topology recommendations, high availability, alerts and more. Challenges in the integration are addressed as HAWQ is not part of the Hortonworks Data Platform stack. The presentation recommends future work for Ambari like supporting automated HAWQ upgrades and enabling dynamic configuration reloads without requiring a service restart.
Zeppelin Interpreters
PSQL (to became JDBC in 0.6.x)
Geode
SpringXD
Apache Ambari
Zeppelin Service
Geode, HAWQ and Spring XD services
Webpage Embedder View
HAWQ is an in-memory, distributed SQL query engine that runs as a Hadoop service. It provides two-way integration with HDFS, Hive, and HBase. HAWQ supports SQL transactions through commands like BEGIN, COMMIT, and ROLLBACK. External tables in HAWQ can be used to query data stored in HDFS files, Hive tables, and HBase tables.
HAWQ: a massively parallel processing SQL engine in hadoopBigData Research
HAWQ, developed at Pivotal, is a massively parallel processing SQL engine sitting on top of HDFS. As a hybrid of MPP database and Hadoop, it inherits the merits from both parties. It adopts a layered architecture and relies on the distributed file system for data replication and fault tolerance. In addition, it is standard SQL compliant, and unlike other SQL engines on Hadoop, it is fully transactional. This paper presents the novel design of HAWQ, including query processing, the scalable software interconnect based on UDP protocol, transaction management, fault tolerance, read optimized storage, the extensible framework for supporting various popular Hadoop based data stores and formats, and various optimization choices we considered to enhance the query performance. The extensive performance study shows that HAWQ is about 40x faster than Stinger, which is reported 35x-45x faster than the original Hive.
Pivotal is a trusted partner for IT innovation and transformation. From the technology, to the people, to the way people interact with technology, Pivotal is transforming how the world builds software.
At Strata NYC 2015, Pivotal, announced it will Supercharge the Hadoop Ecosystem by contributing the HAWQ advanced SQL on Hadoop analytics and MADlib machine learning technologies to The Apache Software Foundation.
The document discusses the new features in Pivotal HD 1.1, including improved high availability for HAWQ and Namenode, new UDF and diagnostic tools for HAWQ, upgraded Apache Hadoop components to version 2.0.5 and 2.0.6, improved Hive, HBase, and Oozie, Kerberos support for security, and new tools like the Unified Storage Service, Data Loader, and Command Center for easier administration.
The document summarizes the journey of HAWQ and MADlib from being proprietary Pivotal technologies to becoming Apache open source projects. It provides an overview of HAWQ, including its key features like SQL compliance, performance advantages over other SQL-on-Hadoop systems, and flexible deployment options. It also summarizes MADlib, describing its machine learning functions and advantages of scalable in-database machine learning. Both projects are now available on open source platforms like Hadoop and aim to advance SQL and machine learning on big data through open collaboration.
Massively Parallel Processing with Procedural Python - Pivotal HAWQInMobi Technology
The document discusses massively parallel processing using procedural Python. It describes EMC Corporation and its subsidiaries which provide data storage, virtualization, security, and other software solutions. It also discusses Pivotal's open source contributions and the architecture of its HAWQ database which allows Python user-defined functions to perform parallel operations across clusters.
The document describes creating and loading sample data into HAWQ internal tables. First, the retail_demo schema and tables are dropped and recreated. Then, sample data files stored in HDFS are copied into the corresponding HAWQ tables using the COPY command in psql. The load is verified by running a script to check the row counts in each table. This demonstrates how to define and populate tables within HAWQ using sample data for testing and analysis.
This document provides an agenda and overview for a presentation on SQL on Hadoop. The presentation will cover various SQL on Hadoop technologies including Hive, HAWQ, Impala, SparkSQL, HBase with Phoenix, and Drill. It will also include an introduction, surveys to collect information from attendees, and discussions on networking and food. The hosts will provide background on their experience with big data and Hadoop.
HAWQ is an enterprise platform that provides the fewest barriers, lowest risk, and fastest way to perform big data analytics on Hadoop. It combines SQL with Hadoop by providing ANSI SQL capabilities on Hadoop for high performance analytics. HAWQ stores all data directly on HDFS and runs on various Hadoop distributions like Pivotal HD, HDP and IBM BigInsights.
Pivotal HAWQ and Hortonworks Data Platform: Modern Data Architecture for IT T...VMware Tanzu
Pivotal HAWQ, one of the world’s most advanced enterprise SQL on Hadoop technology, coupled with the Hortonworks Data Platform, the only 100% open source Apache Hadoop data platform, can turbocharge your analytic efforts. The slides from this technical webinar present a deep dive on this powerful modern data architecture for analytics and data science.
Learn more here: http://pivotal.io/big-data/pivotal-hawq
This document discusses how Hortonworks Data Platform (HDP) can enable enterprises to build a modern data architecture centered around Hadoop. It describes how HDP provides a centralized platform for managing all types of data at scale using technologies like YARN. Case studies are presented showing how companies have used HDP to optimize costs, develop new analytics applications, and work towards creating a unified "data lake". The document outlines the key components of HDP including its support for any application, any data, and deployment anywhere. It also highlights how partners extend HDP's capabilities and how Hortonworks provides enterprise-grade support.
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015NoSQLmatters
There are many frameworks that can offer real time on top of Hadoop. This talk will show you the usage of Pivotal HAWQ and how it is easy to use SQL for querying your Hadoop data. Come and see the power and easy of use that can help you on using the Hadoop ecosystem.
This work presents a P4 compiler backend targeting XDP, the eXpress Data Path. P4 is a domain-specific language describing how packets are processed by the data plane of a programmable network elements. XDP is designed for users who want programmability as well as performance.
https://github.com/williamtu/p4c-xdp/
The document introduces the Windows Azure HDInsight Service, which provides a managed Hadoop service on Windows Azure. It discusses big data and Hadoop, describes the components included in HDInsight like HDFS, MapReduce, Pig and Hive. It provides examples of using Pig, Hive and Sqoop with HDInsight and explains how HDInsight is administered through the management portal.
The event, held on 11th December 2018, was a technical presentation about running MS SQL Server 2017 on Linux. We started off by using containers and proceeded in looking at High Availability and Data Protection, more specifically:
- Supported features & Linux differences
- Installing SQL Server on a Linux Container
- Accessing SMB 3.0 shared storage using Samba
- Setting up a Fail over Cluster using Pacemaker
- Setting up AlwaysOn Availability Groups using Pacemaker
- Authenticating to SQL Server using AD Authentication
- Setting up Read-Scale Cross-Platform Availability Groups
https://techspark.mt/sql-server-on-linux-11th-december-2018/
Apache conbigdata2015 christiantzolov-federated sql on hadoop and beyond- lev...Christian Tzolov
Slides from ApacheCon BigData 2015 HAWQ/GEODE talk: http://sched.co/3zut
In the space of Big Data, two powerful data processing tools compliment each other. Namely HAWQ and Geode. HAWQ is a scalable OLAP SQL-on-Hadoop system, while Geode is OLTP like, in-memory data grid and event processing system. This presentation will show different integration approaches that allow integration and data exchange between HAWQ and Geode. Presentation will walking you through the implementation of the different Integration strategies demonstrating the power of combining various OSS technologies for processing bit and fast data. Presentation will touch upon OSS technologies like HAWQ, Geode, SpringXD, Hadoop and Spring Boot.
EKAW - Publishing with Triple Pattern FragmentsRuben Taelman
Slides for the presentation on Publishing with Triple Pattern Fragments in the Modeling, Generating and Publishing knowledge as Linked Data tutorial at EKAW 2016.
1. HAWQ is an open source MPP database for Hadoop that provides SQL querying capabilities and integration with data in HDFS and other sources.
2. It uses a master-segment architecture with dynamic resource management through YARN to enable high performance SQL queries across large datasets.
3. The document discusses HAWQ's architecture, performance advantages, extensions for querying external data through PXF, and integration with Hive through different connectors and a unified catalog.
2010-01-28 NSA Open Source User Group Meeting, Current & Future Linux on Syst...Shawn Wells
RHEL 5.4 focused on virtualization improvements like full support for KVM hypervisor on x86_64, network performance enhancements through GRO, and storage updates including Ext4 bug fixes and a technology preview of XFS file system support. For System z, RHEL 5.4 included features like support for large volumes, FCP performance monitoring, shutdown action tools, and improved installation workflow.
This document provides an overview of cBPF and eBPF. It discusses the history and implementation of cBPF, including how it was originally used for packet filtering. It then covers eBPF in more depth, explaining what it is, its history, implementation including different program types and maps. It also discusses several uses of eBPF including networking, firewalls, DDoS mitigation, profiling, security, and chaos engineering. Finally, it introduces XDP and DPDK, comparing XDP's benefits over DPDK.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
This document discusses HAWQ's ability to query unmanaged data through its integration with Hive and HCatalog. It provides an overview of HAWQ architecture and how it executes queries involving external data sources via its PXF extension framework. The document describes optimizations HAWQ performs when querying Hive data, such as predicate pushdown and column projection, and how the HAWQ-Hive catalog integration allows queries against Hive metadata without creating external HAWQ tables.
This document provides an overview of Kea DHCP, an open source DHCP server. It discusses Kea's modular design, configuration options at different levels, database backend support for information storage, high availability capabilities including load balancing and hot standby, and extension points through hook libraries. The webinar aims to introduce administrators to Kea's features and capabilities.
The document discusses accelerating Apache Hadoop through high-performance networking and I/O technologies. It describes how technologies like InfiniBand, RoCE, SSDs, and NVMe can benefit big data applications by alleviating bottlenecks. It outlines projects from the High-Performance Big Data project that implement RDMA for Hadoop, Spark, HBase and Memcached to improve performance. Evaluation results demonstrate significant acceleration of HDFS, MapReduce, and other workloads through the high-performance designs.
Open NX-OS provides an extensible, open and programmable network operating system across Nexus 3K/9K platforms. It features enhanced infrastructure layers including open package/application integration using RPM/YUM, an open Linux architecture with kernel networking stack, and an open NX-API REST interface using model-based APIs for automation.
This document provides an overview of Hadoop architecture and the Hadoop Distributed File System (HDFS). It discusses Hadoop core components like HDFS, YARN and MapReduce. It also covers HDFS architecture with the NameNode and DataNodes. Additionally, it explains Hadoop configuration files, modes of operation, commands and daemons.
Suse Enterprise Storage 3 provides iSCSI access to connect to ceph storage remotely over TCP/IP, allowing clients to access ceph storage using the iSCSI protocol. The iSCSI target driver in SES3 provides access to RADOS block devices. This allows any iSCSI initiator to connect to SES3 over the network. SES3 also includes optimizations for iSCSI gateways like offloading operations to object storage devices to reduce locking on gateway nodes.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
3. Motivations: SQL on Hadoop
RDBMS
?
various formats, storages
supported on HDFS
● ANSI SQL
● Cost based optimizer
● Transactions
● ...
Foreign
Tables!
4. PXF is an extension framework that facilitates access to external data
● Uniform tabular view to heterogeneous data sources
● Exploits parallelism for data access
● Pluggable framework for custom connectors
● Provides built-in connectors for accessing data in HDFS files, Hive/HBase
tables, etc
What is PXF ?
6. Deployment Architecture
HAWQ
Master Node NN
pxf
HBase
Master
DN4
pxf
HAWQ
seg4
DN1
pxf
HAWQ
seg1
HBase
Region
Server1
DN2
pxf
HAWQ
seg2
HBase
Region
Server2
DN3
pxf
HAWQ
seg3
HBase
Region
Server3
* PXF needs to be installed on all DN
* PXF is recommended to be installed on NN
7. PXF Components
Fragmenter
Splits dataset into partitions
Returns locations of each partition
Accessor Understand and read/write the fragment
Return records
Resolver Convert records to a consumable format (Data Types)
Compact way to configure Fragmenter, Accessor,
ResolverProfile
8. Architecture - Read Data Flow
HAWQ
Master Node NN
pxf
DN1
pxf
HAWQ
seg1
select * from ext_table0
getFragments() API
pxf://<location>:<port>/<pa
th>
1
Fragments (JSON)2
7
3
Assign
Fragments
to Segments
DN1
pxf
HAWQ
seg1
DN1
pxf
HAWQ
seg1
Query dispatched to Segment 1,2,3… (Interconnect)
5
Read() REST
6 records
8
query result
Records (stream)
Fragmenter
Resolver
Accessor
4
10. 1. Get Fragments (Partition Data)
2. Fragment Distribution
3. Reading Data
HAWQ Bridge - Deep Dive
11. Step 1 - Get Fragments
• Code location: https://github.com/apache/incubator-
hawq/blob/master/src/backend/access/external/hd_work_mgr.c
• Called by optimizer (createplan.c)
• Gets fragments from PXF for the given location specified in the table,
using Fragmenter.
12. Step 2 - Fragments Distribution
• Code location: hd_work_mgr.c
• Returns a mapping of the fragments for each segment.
• Trying to maximize both parallelism and locality:
• Splitting the load between all participating segments (determined by
GUC).
• Assigning fragments to segments with a replica on the same host.
14. Step 3 - Reading Data
• Done using external protocol API.
• PXF code is under cdb-pg/src/backend/access/external/
• C Rest API using enhanced libcurl https://github.com/apache/incubator-
hawq/blob/master/src/backend/access/external/libchurl.c
• Each segment calls PXF to get each of its fragments’ data, using
Accessor & Resolver
• Data returned as stream(text/csv/binary) from PXF
18. PXF HDFS Plugin
Fragment - Splits (blocks)
● Support Read : multiple formats
● Support Write to Sequence Files
● Chunked Read Optimization
● Support for stats
Profile Description
HdfsTextSimpl
e
Read delimited single line records (plain text)
HdfsTextMulti Read delimited multiline records (plain text)
Avro Read avro records
JSON Supports simple/pretty printed JSON with field
projection
ORC* Supports ORC files with Column Projection &
Filter Pushdown
19. PXF Hive Plugin
Fragment - Splits of the file stored in table
● Text based
● SequenceFile
● RCFile
● ORCFile
● Parquet
● Avro
➔ Complex types are converted to text
Profile Description
Hive Read all Hive tables (all types)
HiveRC Hive tables stored in RC (serialized with
ColumnarSerDe/LazyBinaryColumnarSerDe)
HiveText Faster access for Hive tables stored as Text
HiveORC Supports ORC files with Column Projection & Filter
Pushdown
20. PXF HBase Plugin
Fragment - Regions
● Read Only. Uses Profile ‘Hbase’
● Filter push down to Hbase scanner
○ (Operators: EQ, NE, LT, GT, LE, GE & AND)
● Direct Mapping
● Indirect Mapping
○ Lookup table - pxflookup
○ Maps attribute name to hbase <cf:qualififer>
(row key) mapping
sales id=cf1:saleid
sales cmts-cf8:comments