A ETL procedure in three stages (Extraction, Transformation and Loading) and some ETL tools the proper implementation of this concept, are the solution to the needs of the institutions to properly manage your data.
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It's about store information efficiently. Unclassified data creates problems when you find it. The user needs to know what data he manages, where they are and how to extract them.. It may seem that the difficult thing is to make decisions based on data, but no, finding the data itself is often much more complicated.
But nevertheless, ETL tools are the solution to this problem.
What is an ETL?
The acronym ETL comes from Extracting, Transforming, Loading, which describes very well the idea of what what is an ETL. The ETL tools They were created to boost and facilitate data storage.
To find out what an ETL is, it is best to check how is an ETL procedure. It's about the next steps:
- Start
- Build reference data
- Sources extract
- Validate
- To transform
- Load into tables
- Make audit reports
- To post
- proceedings
- Clean up
Sometimes, these steps are monitored and performed indirectly, but they are time consuming and the result may not be exact. The purpose of using ETL tools is to save time and make the whole procedure more reliable.
What are the traditional key features of ETL tools?
ETL tools automate data extraction operations from source systems, transformation for analytical and processing uses and subsequent loading at the destination. whatever the selected system and regardless of the type of environment it is. Your intervention simplifies the ETL procedure compared to manual integration scripts in SQL or other programming languages.
To find out what an ETL is, you need to understand the internal configuration, the capabilities and features of ETL tools.. Among the most important, it is worth highlighting the following:
- Compatibility with the integration of data stored in local systems and in the cloud, including hybrid cloud environments.
- Ability to connect and extract data from a range of sources. such as applications, databases, big data systems based on technologies such as Hadoop and Spark and flat file repositories, among others.
- Data profiling functions, that make it possible to perform an analysis of the consistency of the data already at the source and before starting the ETL procedure, being able to also examine the existence of dependencies and other attributes of the data.
- Development capabilities team-based enabling effective collaboration on integration initiatives.
- Quality and data cleaning features, that increase its reliability.
- Capabilities for data synchronization to maintain consistency between systems.
- Data transformation capabilities, that can include everything, from reformatting to conversion and from workflow orchestration to data mapping.
- Metadata management support.
Don't confuse ETL with an equivalent definition, ELT, that reverses the final stages of the procedure, loading before transformation. An option that handles the manipulation of the data once it is already on the target system.
It is a capacity especially recommended for big data applications where large volumes of raw data are often loaded into Hadoop, Spark or other repositories, and then filtered according to the needs of different analytical uses.
Can Big Data Make ETL Tools Disappear?
Short term, ETL tools won't go away, but the focus of ETL tools will change from “site to data”.. There will still be a place for ETL tools, either as standalone ETL tools or, less frequently, as residual mid-level ETL tools.
Increasingly, this emerging model needs a single central repository for all business information. In other words, a place for mass storage. This could be Hadoop, Cassandra o Spark, functioning as a distributed file system, or actually, a cloud storage service like S3. It is also necessary to accentuate the movement of smaller derived data sets., from this repository, to the source systems that compose it.
The role of ETL tools will continue to grow, not only in proportion to the volume of data, but also It should also encompass the explosion of data variety that machine-generated data is causing... At the same time, with the need to increase the speed of analysis-based decision making, the ETL tool pipeline should move from batch operation to as close to real time as possible.
Traditional ETL tools and data integration providers like Informatica are adapting their products and adapting their engines to use Hadoop, Spark and other Big Data platforms, and add the ability to move data in and out of Hadoop.
