Quick Read: Overview on ETL Tools and Processes 2019

BIG DATA AND THE PROBLEMS DEALING WITH IT

90% of today’s data has been created in the last two years only. According to Domo, our current data output is roughly 2.5 quintillion bytes per day. Data has become an essential topic and a key factor to business success. Especially, processing them the right way has become a crucial solution for many businesses around the world.

Only well structured, analyzed and interpreted data could give powerful business and market insights. Traditional tools didn’t provide an appropriate solution for making data available for analysis quickly and easily. Data integration solutions were designed to meet the most important part of an organization’s BI requirements. To fulfill this, some ETL vendors evolved to a complete data management solution others became an end-to-end business intelligence solution.

ETL PROCESS

ETL is the abbreviation for Extract, Transform and Load. And what these tools basically do is to pull out data from one or even multiple database(s) and place them in one database, the so-called target storage. An ETL tool is defined as a system that extracts, cleans, conforms, and delivers source data into a target storage so it can be used for queries and analysis. Their main task is to deliver reliable and accurate data that can be used for crucial business decisions.

ETL process

HOW DOES THE ETL PROCESS WORK?

Extract

The extract step covers the data extraction from multiple source systems and the preparation of the data for the next steps. The main objective of this step is to retrieve all the required data from the source systems with as little resources as possible and without negatively effecting the performance and response time of the source system.

Transform

The transform step applies a set of rules to convert all extracted data to the same dimension using the same units so that it can later be joined together. The transformation step also gathers data from several sources, generating new calculated values and applying advanced validation rules.

Load

During the loading step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. During the loading process the data is written into the target database.

WHAT ARE THE BENEFITS OF ETL?

The main benefit of ETL is that it is much easier and faster to use than traditional methods which move data by writing codes manually. ETL tools contain graphical interfaces which speed up the process of mapping tables and columns between the source and target storages.

Some key advantages of ETL tools:

Ease of Use:

Automated Processes make coding unnecessary. The tool automatically identifies the types and formats of the data, sets the rules how the data has to be extracted and processed.

Visual flow:

The graphical user interface (GUI) visualizes the data process / the system’s logic for you.

Operational resilience:

A build-in error handling functionality ensures a resilient and well instrumented ETL process.

Good for complex data management situations:

ETL tools simplify the task and assist you with data analysis, string manipulation, data changes and integration of multiple data sets.

Advanced data profiling and cleansing

Meet transformation requirements of structurally complex data warehouses.

Enhanced business intelligence:

Improved access to information directly impacts the strategic and operational decisions that are based on data driven facts.

High return on investment (ROI):

ETL tools help business to save costs and thereby, generate higher revenues.

Performance:

ETL tools include performance enhancing technologies to build a high-quality data warehouse.

The Complete ETL Tools List

  • Oracle Warehouse Builder (OWB)
  • SAP Data Services
  • IBM Infosphere Information Server
  • SAS Data Management
  • PowerCenter Informatica
  • Elixir Repertoire for Data ETL
  • Data Migrator (IBI)
  • SQL Server Integration Services (SSIS)
  • Talend Studio for Data Integration
  • Sagent Data Flow
  • Alteryx
  • Actian DataConnect
  • Open Text Integration Center
  • Oracle Data Integrator (ODI)
  • Cognos Data Manager
  • Microsoft SQL Server Integration Services (MSSIS)
  • CloverETL
  • Centerprise Data Integrator
  • IBM Infosphere Warehouse Edition
  • Pentaho Data Integration
  • Adeptia Integration Server
  • Syncsort DMX
  • QlikView Expressor
  • Realtional Junction ETL Manager (Sesame Software)
  • Data Virtuality Pipes
  • Fivetran
  • Stich Data
  • Xplenty
  • alooma
  • Treasure Data
  • Panoply
  • Matillion
  • etleap
  • FlyData
  • Keboola
  • Skyvia
  • Astronomer
  • openbridge
  • improvado
  • Blendo

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