What is ETL (Extract, Transform, Load) in Big Data?

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Overview

Nowadays by seeing the current market situation, data processing of structured and unstructured data becomes a very crucial part of an effective business. Business partners are investing more in data processing since the amount (volume) and variety of raw data increases very rapidly.

Since the last decade, the ETL (Extract Transform Load) process has become fruitful to flow business processes smoothly. Data mining and Data Warehousing are the key concepts for analyzing big data. Also to make the best business decisions and strategies which can increase revenue and profit of an organization.

What is ETL?

ETL is a process that extracts, transforms, and loads data from multiple sources to a data warehouse or other unified data repository.

In the world of data warehousing, if you need to bring data from multiple different data sources into one, centralized database, you must first:

EXTRACT data from its original source
TRANSFORM data by deduplicating it, combining it, and ensuring quality, to then
LOAD data into the target database

How does ETL Work?

Let’s deep dive & see how the ETL process is start, & what it exactly does.

Data Fetching(Extraction):

In simple words, fetch data from multiple resources as per the client’s requirement.

The most important step for a successful ETL Process is to extract data and process data from various data sources
What to extract depends on the available data sources and business requirements.
To filter the raw data, validation rules are apply in the Extraction process
It is not a one-time process, changes are apply from the source at a regular time period.
It is not necessary to store data directly to the traditional data warehouses sometimes according to requirement it is stored into the “Warehouse Staging Area” which is a temporary data storage

Data Transformation :

Extracted Data needs to be transform into a structured format that is compatible with the predefined Data Warehouse.
Structured data is easy to process further as a developer is aware about the meta information of data
steps that are apply to transform data:

After completion of each step, data will be store into a staging area for further processing.
In case of any failure, the transformation process will be resume from staging.

Load:

Structured/Transformed data will be loaded to its appropriate table in the Data Warehouse.
Lodding of transformed data can be performed in two ways:
- Record By Record
- Bulk Load
Bulk load is the most preferable strategy to load data in the warehouse in order to improve performance

Open Source ETL Tools for Data Integration

Apache Airflow –

Apache Airflow is a platform that allows you to programmatically author, schedule and monitor workflows. The tool enables users to author workflows as directed acyclic graphs (DAGs)

Apache NiFI –

Apache NiFi is a system used to process and distribute data and offers directed graphs of data routing, transformation, and system mediation logic.

Kafka

Apache Kafka is a distributed streaming platform that enables users to publish and subscribe to streams of records, store streams of records, and process them as they occur. Kafka is most notably used for building real-time streaming data pipelines

The benefits of ETL

Improve quality by performing data cleansing prior to loading the data to a different repository
Reduce Unnecessary Expenses
Validate Data Before Migration
If your data storage is a cost-sensitive system, you can use ETL to keep the storage costs low