Pentaho Data Integration Introduction
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This document summarizes Pentaho Data Integration (Kettle), an open source data integration tool. It discusses Kettle's capabilities for extracting, transforming, and loading data from various sources. Key features include its graphical user interface, support for over 35 database types, flexible transformation capabilities, and large community of users. The document also notes Kettle's use in big data and Hadoop environments and its adoption in small, medium, and large enterprises.
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given 2020 at Technical University of Kaiserslautern, Germany
topic: big data and frameworks
INF2190_W1_2016_public
This document provides an introduction and overview of the INF2190 - Data Analytics course. It discusses the instructor, Attila Barta, details on where and when the course will take place. It then provides definitions and history of data analytics, discusses how the field has evolved with big data, and references enterprise data analytics architectures. It contrasts traditional vs. big data era data analytics approaches and tools. The objective of the course is described as providing students with the foundation to become data scientists.
Building the Data Lake with Azure Data Factory and Data Lake Analytics
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Is the traditional data warehouse dead?
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
Building big data solutions on azure
See what Microsoft Azure cloud offers you to get started and build you own Big Data solution on the cloud
Meeting today’s dissemination challenges – Implementing International Standar...
This document discusses the .Stat system, which serves as a central repository for validated statistics and metadata. .Stat connects data production, sharing, and dissemination processes. It provides three key functional areas: a data upload engine, a data delivery engine, and a data browser. .Stat can be mapped to stages in the Generic Statistical Business Process Model and incorporates standards like SDMX for dissemination, data exchange, and internal data sharing. The document outlines .Stat's current role and future plans to further support SDMX artifacts, ingest, registries, and semantic web opportunities.
Big data and oracle
The document discusses Big Data architectures and Oracle's solutions for Big Data. It provides an overview of key components of Big Data architectures, including data ingestion, distributed file systems, data management capabilities, and Oracle's unified reference architecture. It describes techniques for operational intelligence, exploration and discovery, and performance management in Big Data solutions.
Qo Introduction V2
The document discusses trends in data growth and computing. It notes that the amount of data being stored doubles every 18-24 months and provides examples of large data holdings from companies like AT&T, Google, and Walmart. It then summarizes key points about data growth from enterprises and digital lives. The rest of the document focuses on strategies and technologies for managing large and growing volumes of data, including parallel processing databases, new database architectures, and the QueryObject system.
Hd insight overview
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Eclipse day Sydney 2014 BIG data presentation
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. This document discusses big data challenges, growth drivers, opportunities, and new approaches to processing and analytics using tools like Hadoop, MongoDB, and BIRT. It provides an overview of how BIRT supports connecting to and visualizing data from different sources, including Hive, Cassandra, and MongoDB, and how the BIRT community contributes to its open source development.
How to Quickly and Easily Draw Value from Big Data Sources_Q3 symposia(Moa)
This document discusses how MicroStrategy can help organizations derive value from big data sources. It begins by defining big data and the types of big data sources. It then outlines five differentiators of MicroStrategy for big data analytics: 1) enterprise data access with complete data governance, 2) self-service data exploration and production dashboards, 3) user accessible advanced and predictive analytics, 4) analysis of semi-structured and unstructured data, and 5) real-time analysis from live updating data. The document demonstrates MicroStrategy's capabilities for optimized access to multiple data sources, intuitive data preparation, in-memory analytics, and multi-source analysis. It positions MicroStrategy as a scalable solution for big data analytics that can meet
An Overview of VIEW
The document describes scientific workflows for big data and the challenges they present. It discusses Prof. Shiyong Lu's work on developing the VIEW system for designing, executing, and analyzing scientific workflows. The VIEW system provides a runtime environment for workflows, supports their execution on servers or clouds, and enables efficient storage, querying and visualization of workflow provenance data.
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The document discusses trusting a knowledge management (KM) and enterprise content management (ECM) strategy to Microsoft SharePoint. It outlines SharePoint's capabilities that enable it to effectively support large-scale content management activities, including rich metadata structures, taxonomy, security features, workflows, search, integration with line of business systems, and support for multiple languages. Governance is required to balance control with flexibility when adopting SharePoint as an information management platform.
Modernizing Your Data Warehouse using APS
The document discusses modernizing a data warehouse using the Microsoft Analytics Platform System (APS). APS is described as a turnkey appliance that allows organizations to integrate relational and non-relational data in a single system for enterprise-ready querying and business intelligence. It provides a scalable solution for growing data volumes and types that removes limitations of traditional data warehousing approaches.
Jonathan Ralton - Trusting Your KM & ECM Strategy To SharePoint
SharePoint is one of the most widely deployed repositories, given its showcase characteristics: being highly scalable, searchable, and secure.
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Big Data Analytics: From SQL to Machine Learning and Graph Analysis
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Pentaho Data Integration Introduction
- 6. 650 pages
- 7. Pentaho Data Integration for BI Business Intelligence! That's what we do.
- 8. Pentaho Data Integration – Kettle K ettle E xtraction T ransportation T ransformation L oading E nvironment
- 11. XML files
- 12. XLS files
- 13. Xbase files (dBase, Foxpro, etc)
- 15. Generated data
- 16. MS Access files
- 17. LDAP
- 18. Geo-data
- 19. ...
- 22. partitioning
- 23. merging
- 24. joining
- 25. duplicating
- 26. clustering (MPP)
- 28. files
- 31. Mapping
- 32. Selecting
- 33. Filtering
- 34. Pivotting ...
- 38. Bulk loading
- 39. Parallel loading
- 40. Clustering
- 42. Debugger
- 47. ...
- 50. All regions on Earth
- 51. Meet on our Forum : +40,000 posts in 10,000 threads in 4 years
- 52. Use our JIRA case tracking systems
- 53. Download more than 10,000 copies of Kettle per month http://www.ohloh.net/projects/3624?p=Kettle http://www.softpedia.com/progClean/Kettle-Clean-80094.html
- 55. Export data from database to text-file or more other databases
- 56. Data migration between database applications
- 57. Exploration of data in existing databases (tables, views, etc.)
- 58. Information improvement using lookups
- 59. Data cleaning
- 64. ...
- 68. Natural fit for additional data sources, targets and transformations
- 70. Download free study at pentaho.com
- 73. From Tera-bytes to Peta-bytes
- 74. Big Data stored in Hadoop (MapReduce) / HDFS / Hive
- 75. Reduces complexity for developers
- 76. Leverages standard components like Pentaho Data Integration
- 77. Drag & drop creation of map and reduce transformations
- 79. Presentation + Demo : http://vimeo.com/14641559
- 82. Case tracker: http://jira.pentaho.org/browse/PDI
- 83. Continuous Integration Server: http://ci.pentaho.com/job/Kettle
- 84. Wiki : http://wiki.pentaho.org/ display/EAI
- 85. IRC Channel: ##pentaho (on Freenode)
- 86. Mailing list: http://groups.google.com/group/kettle-developers
- 87. My blog: http://www.ibridge.be
- 88. My coordinates: mcasters at pentaho dot org
- 89. Pentaho Books