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ETL (Extract, Transform, Load)

ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

ETL provides the foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL cleanses and organizes data in a way which addresses specific business intelligence needs, like monthly reporting, but it can also tackle more advanced analytics, which can improve back-end processes or end user experiences. ETL is often used by an organization to:
-Extract data from legacy systems
-Cleanse the data to improve data quality and establish
consistency
-Load data into a target database

How ETL works?
The easiest way to understand how ETL works is to understand what happens in each step of the process.

Extract
During data extraction, raw data is copied or exported from source locations to a staging area. Data management teams can extract data from a variety of data sources, which can be structured or unstructured. Those sources include but are not limited to:
-SQL or NoSQL servers
-CRM and ERP systems
-Flat files
-Email
-Web pages

Transform
In the staging area, the raw data undergoes data processing. Here, the data is transformed and consolidated for its intended analytical use case. This phase can involve the following tasks:
Filtering, cleansing, de-duplicating, validating, and authenticating the data.
Performing calculations, translations, or summarizations based on the raw data. This can include changing row and column headers for consistency, converting currencies or other units of measurement, editing text strings, and more.
Conducting audits to ensure data quality and compliance
Removing, encrypting, or protecting data governed by industry or governmental regulators
Formatting the data into tables or joined tables to match the schema of the target data warehouse.

Load
In this last step, the transformed data is moved from the staging area into a target data warehouse. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes and, less often, full refreshes to erase and replace data in the warehouse. For most organizations that use ETL, the process is automated, well-defined, continuous and batch-driven. Typically, ETL takes place during off-hours when traffic on the source systems and the data warehouse is at its lowest.

What is business intelligence?
Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing.

Why business intelligence is important?
Business intelligence gives organizations the ability to ask questions in plain language and get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Business intelligence best practices
Organizations benefit when they can fully assess operations and processes, understand their customers, gauge the market, and drive improvement. They need the right tools to aggregate business information from anywhere, analyze it, discover patterns and find solutions.

The best BI software supports this decision-making process by:

Connecting to a wide variety of different data systems and data sets including databases and spreadsheets.
Providing deep analysis, helping users uncover hidden relationships and patterns in their data.
Presenting answers in informative and compelling data visualizations like reports, maps, charts and graphs.
Enabling side-by-side comparisons of data under different scenarios.
Providing drill-down, drill-up and drill-through features, enabling users to investigate different levels of data.

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