Data Warehousing Tutorials
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Data Warehouse is a collection of software tool that help analyze large volumes of disparate data. The goal is to derive profitable insights from the data. This course covers advance topics like Data Marts, Data Lakes, Schemas amongst others.
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Building next generation data warehouses
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
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A light introduction to recent discussion revolving around NOSQL and relational databases
http://www.slideshare.net/PedroFG
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Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
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Recommended
Building next generation data warehouses
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
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* NoSQL
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SLQ vs NOSQL - friends or foes
A light introduction to recent discussion revolving around NOSQL and relational databases
http://www.slideshare.net/PedroFG
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Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
its a guide about data science .. a patheay about rescources required to become one
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This ppt gives information about:
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Are you a young professional who just got out of college and unsure which career path to follow? Are you thinking about changing your career to something completely new and looking for options? Either way, this webinar is the right one for you. It’s the first in a series that the new ODTUG Career Track Community will bring you to show what Oracle careers look like and where/how to start with them.
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The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
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Introduction To OLAP
Data Warehousing Architecture
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Description:
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In ETL Testing, the following are validated -
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2) The ETL Job that is designed to extract data from Source tables and then move them to staging tables. (Transform process)
3) Data validation within the Staging tables to check all Mapping Rules / Transformation Rules are followed.
4) Data Validation within Target tables to ensure data is present in required format and there is no data loss from Source to Target tables.
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Data Warehousing Tutorials
- 2. • Training Summary • Data Warehouse is a collection of software tool that help analyze large volumes of disparate data. The goal is to derive profitable insights from the data. This course covers advance topics like Data Marts, Data Lakes, Schemas amongst others. • What should I know? • The tutorials are designed for beginners with little or no Data Warehouse Experience. Though basic understanding of Database and SQL is a plus.
- 3. Course Syllabus • What Is Data Warehousing? Types, Definition & Example • Data Warehouse Concepts, Architecture and Components • ETL (Extract, Transform, and Load) Process • ETL vs ELT: Must Know Differences • What is Data Modelling? Conceptual, Logical, & Physical Data Models • What is OLAP (Online Analytical Processing): Cube, Operations & Types • MOLAP (Multidimensional Online Analytical Processing) • OLTP vs OLAP: What's the Difference? • What is Dimensional Model in Data Warehouse? • Star and SnowFlake Schema in Data Warehousing • Data Mart : What is Data Mart, Types & Example • Data Warehouse vs Data Mart: Know the Difference • What is Data Lake & It's Architecture • Data Lake vs Data Warehouse: Know the Difference • What is Business Intelligence? Definition & Example • Data Mining : Process, Techniques, Tools & Examples • Difference between Data Mining and Data Warehouse • Difference Between Fact Table and Dimension Table