Data Warehousing and ETL Tools
Ranked #30,311 in Computers & Electronics, #564,359 overall
Data warehousing, ETL and data integration applications and tools in business intelligence
ETL (which stands for extraction transformation loading) tools are made to simplify work with data integration, data migration and data transformation according to the ETL model and business intelligence strategy of an organization. The main purpose of having an ETL application in an organization is to use it to feed a data warehouse and provide information (data) for business intelligence and reporting systems.
ETL Tools overview
ETL (Extract, transform, load)
ETL is a process which helps get the data from the source systems of any organization, transform it and load the data into a data warehouse which is a centrally manageable and integrated database which accumulates data from the source systems and operational sources.
To say it simpler, the main goal of ETL is to exchange the source data for valuable information.
ETL tools are made to simplify work with data integration, data migration and data transformation according to the ETL model.
In the simpliest form ETL tools make the transformation and transfer of the data from the source systems to target systems in poking mode, by actual time or through the action schedule.
There are many benefits from using ETL tools but first let's try to decode the contraction of ETL.
1. E - Extract
Extract means to get something from somewhere and in the case of ETL it means getting the data from one or more outside sources like data warehouse. The assignment of this stage rests on the identification of data (tables, relation table fields), which meet the requirements of target system.
Most of the ETL tools support such data sources as relational databases, indexed files, sequential files, flat files, XML documents, external data source, business aplications and webservices.
Detailed analysis of the Extraction phase.
2. T - Transform
Transform the data to make it compatible with an integrated data structure in a data warehouse or data mart. Another task of the Transform is source's fusion in one integrated target structure and creation of the initial data's model or addition of the new sources to already extant model.
Detailed analysis of the Data transformation phase.
3. L - Load
Load is the third stage of ETL which rests on physical data's transfer to target integrated data base i.e. loading of the data to data base.
More on Loading the data into a data warehouse.
ETL tools support creation and maintenance of a data warehouse service of a company. ETL Tools automate the data sources extraction process, data standardization, mapping, matching and duplicate retrieval through the integration with external tools. ETL also supports decoding, transformation, aggregation and loading target databases or applications.
The another outcome of using an ETL tool is connected with costs, the time and amount of labour, integration, increase of productivity and with the lesser risk because of the shorter time of realization of the project, lesser costs and easier system's support and maintenance.
The main areas where the ETL is available:
Data warehouse
Data integration
Data migration and system consolidation
Data synchronization between the systems
Automation of data transmission's processes
Support of building the Business Intelligence systems
Support for the customer segmentation processes
ETL is a process which helps get the data from the source systems of any organization, transform it and load the data into a data warehouse which is a centrally manageable and integrated database which accumulates data from the source systems and operational sources.
To say it simpler, the main goal of ETL is to exchange the source data for valuable information.
ETL tools are made to simplify work with data integration, data migration and data transformation according to the ETL model.
In the simpliest form ETL tools make the transformation and transfer of the data from the source systems to target systems in poking mode, by actual time or through the action schedule.
There are many benefits from using ETL tools but first let's try to decode the contraction of ETL.
1. E - Extract
Extract means to get something from somewhere and in the case of ETL it means getting the data from one or more outside sources like data warehouse. The assignment of this stage rests on the identification of data (tables, relation table fields), which meet the requirements of target system.
Most of the ETL tools support such data sources as relational databases, indexed files, sequential files, flat files, XML documents, external data source, business aplications and webservices.
Detailed analysis of the Extraction phase.
2. T - Transform
Transform the data to make it compatible with an integrated data structure in a data warehouse or data mart. Another task of the Transform is source's fusion in one integrated target structure and creation of the initial data's model or addition of the new sources to already extant model.
Detailed analysis of the Data transformation phase.
3. L - Load
Load is the third stage of ETL which rests on physical data's transfer to target integrated data base i.e. loading of the data to data base.
More on Loading the data into a data warehouse.
ETL tools support creation and maintenance of a data warehouse service of a company. ETL Tools automate the data sources extraction process, data standardization, mapping, matching and duplicate retrieval through the integration with external tools. ETL also supports decoding, transformation, aggregation and loading target databases or applications.
The another outcome of using an ETL tool is connected with costs, the time and amount of labour, integration, increase of productivity and with the lesser risk because of the shorter time of realization of the project, lesser costs and easier system's support and maintenance.
The main areas where the ETL is available:
Blog Posts from Google
- Birst to Help Midsize Enterprises Wrangle Hadoop, Big Data
- Birst, a software as a service business intelligence and analytics provider, has announced support for Hadoop to help enterprises, particularly midsize organizations, wrangle big data. With more and more organizations looking at Apache Hadoop to ...
- Birst Looks To Ease Hadoop Adoption Concerns
- ... (ETL) processes and lots of effort ? something that has prevented midsize organisations from making big data actionable. Birst has branched out to extend its data warehouse technology and business analytics capabilities with big data integration.
- Maximizing the Business Value of Big Data
- Although data integration and more specifically ETL (extract, transform, and load) processing - is at the center of this information revolution, its significance as a critical component to the 'Big Data' engine is often overlooked.
- Birst Makes Big Data Accessible To Midsize Organizations
- Business users can now aggregate and visualize big data such as website interactions, social media and cloud traffic quickly and easily which traditionally would have required extensive ETL processes and considerable pain to set up-something that has ...
ETL process web resources
The top ETL and Data warehousing links
- ETL tools comparison
- The portal presents research on ETL and Data Integration based on Gartner's data integration magic quadrant, forrester researches and author's professional experience.
- ETL Tools
- The hub about ETL tools explains what ETL process is and shows the relation between data integration and data warehousing
- ETL & Data Integration
- ETL, Data Integration and Data Warehousing portal. Includes tutorials on such tools as Datastage, SAS and Informatica PowerCenter.
by dsjohn
dsjohn
Hello,
welcome on my ETL and data integration site
- 0 featured lenses
- Winner of 2 trophies!
- Top lens »
Feeling creative?
Create a Lens!
