What is an ETL Tool: Decoding its Need and Use Cases

on Data Integration • July 12th, 2019 • Write for Hevo

What is an ETL tool?

ETL stands for Extract, Transform and Load. In simple terms, ETL would perform the following steps:  

  1. Data is extracted from one or many sources into a staging area 
  2. Within the staging area, data is transformed into usable formats by converting data types, combining fields, etc. depending on the business use case
  3. Finally, the transformed data is loaded to a destination – often a data warehouse

Just this definition would not help completely understand what is an ETL tool.  However, the answer to the next question will. 

Why use an ETL tool?

Data is often scattered across different systems and applications. Companies may have client and product information in a CRM, such as Salesforce, the accounting data may be in Quickbooks, legacy data stored in Excel spreadsheets, and the website transactions may be in a database like MySQL. 

In order to derive meaningful insights that can grow the business, it would be necessary to bring data from all these disparate data sources together in a useable format, to a single source of truth – a Data Warehouse.

ETL tools have been developed in response to a clear need for methodologies to simplify and enhance the process of getting the raw data scattered across multiple systems into a data analytics warehouse.  

To be able to help you understand what is an ETL tool, this article will dive into some of the use cases where an ETL tool will be used. The article also gives an overview of the ETL tools available and end with a checklist of what you need to look for when evaluating an ETL tool.

Use Cases for ETL Tools:

Building a Data Lake

A Data Lake is a central repository used to store data in its raw format. For instance, some of the key sources of data are unstructured or semi-structured. For example, text messages, web pages, video, other multimedia are all examples of unstructured data. A data lake is built for a use case where there is no need to define a schema prior to getting the data into a data lake. This means companies can store all the data for future use without having to know what kind of business intelligence questions they may have to answer. 

An ETL tool can help bring data from disparate data sources into the data lake in a hassle-free fashion.

Building a Data Warehouse

In today’s world, this has become one of the most common use cases for ETL.

Data Warehouse is a structured environment. Data from the various data sources used by the business will need to be cleaned, enriched and transformed before it can be loaded to the warehouse. Once in the warehouse, this data becomes a ‘single source of truth’ for the company. The key step in setting up a data warehouse is to ensure that the data loaded is indeed accurate and up-to-date and will function as the needed ‘single source of truth’. 

An ETL tool can facilitate the above use case with ease and produce trustworthy data load.

Data Migration

When businesses decide to move from Legacy systems to an updated infrastructure, they rely on an ETL tool to help with the heavy Data Migration involved. This might include extracting the data from source systems, transforming it to a format the new system understands, and loading this to the new infrastructure. Data migrations are often a one-time affair.  

Why are ETL Tools needed?

Now that you understand what is an ETL tool, the next step is to understand – Why are they needed?

All the above use cases can be achieved without using an ETL tool as well. Many businesses attempt building a custom solution to solve this problem. However, there are many reasons that make it hard to be 100% successful at it. Here is why ETL tools prove to be a better alternative.

  • Building custom code for ETL is not a straightforward process. However, there are way too many caveats, complexities, and difficulties in monitoring the accuracy and consistency of data. Any misses there can cause irreparable data loss
  • As the business expands, new data sources come on the radar and will need to be added to the data warehouse. This adds to the engineering workload and would be hard to achieve in an ad-hoc fashion
  • Cost and overhead of resources needed to maintain custom ETL scripts and infrastructure is super high

A powerful ETL tool streamlines all the ETL process and minimises the overhead. A reliable ETL tool will also come with built-in monitoring and alert system which keeps the data infrastructure abreast of any breakdowns or hitches. All of this combined will give reliable, consistent and accurate data so that businesses can focus on deriving meaningful insights. 

Types of ETL Tools available in the market

The ETL tools that are available today can be classified based on two dimensions: Batch Vs Real-time and OnPremise Vs Cloud. Each of these serves a unique purpose.

Batch ETL tools Vs Realtime ETL Tools

A traditional method of getting the data to a destination is to use batch processing. The data will be extracted, transformed and loaded into the data warehouse in batches of ETL jobs. This is cost effective as it consumes limited resources in a time-bound manner. Some of the top batch ETL tools are:

Today, the need to collect and analyze the data in the shortest possible time has increased. Whatever be the data source, it needs to be cleaned, enriched and loaded to the destination in real-time. This is where the need for a real-time data integration tool came into play. Real-time ETL tools help get the most efficient time-to-insight ratio. 

The top Real-time ETL tools available are as follows:

On-Premise Vs Cloud ETL tools

Many businesses run on legacy systems that have both the data and the warehouse set up on premise. This is mostly implemented from a data security perspective – where the data does not go out of the network of the organization. In such cases, businesses prefer having an ETL solution that is compatible to run on-premise. Here are some of the top on-premise ETL tools:

On the other hand, new-age businesses have all their data residing on various applications hosted on the cloud. Given the data now resides on the cloud, businesses are increasingly moving to cloud data warehouse that allows them to leverage the flexibility and agility that the cloud infrastructure offers. 

A cloud ETL tool is built to enable easy data movement from the data sources used by new-age businesses to a cloud destination. Here are some of the top cloud ETL tools:

What to look for when evaluating an ETL tool?

A strong ETL tool will be an invaluable part of the data analytics stack of a data-driven business. The ETL tool selected should connect to all the data sources used by the company; have a glitch-free work interface, and provide a reliable, accurate and secure data load.

The following set of questions will help you select an ETL tool:

  • What are the different data sources that the tool can bring data from?
  • Are there any limits on the scale/volume of data the tool can handle?
  • How does the tool handle errors? Does it ensure data consistency and accuracy? 
  • How smooth and efficient are its data transformation capabilities?
  • How easy is the tool to use?
  • How smooth is the and how soon can the project see the light of the day? 

Hevo Data for ETL

Hevo Data Pipeline as a Service (HevoData.com) can help companies bring data from various sources (Databases, Event Stream, Cloud Applications) into the Data Warehouse in Real-time without writing any code. Hevo is the right ETL partner for businesses for the following reasons: 

  1. Easy Set Up: Hevo pretty much works out of the box. With Hevo, companies can bring their data warehousing projects to life in a matter of a few minutes
  2. 100s of Data Sources: Hevo natively integrates with a variety of data sources – Databases, Sales and Marketing Applications, SDKs, Webhooks and more making it super easy to bring any data to the destination
  3. Automatic Schema Detection and Evolution: Hevo automagically detects the schema of the incoming data and maps it to the warehouse. In case the schema changes in the future, Hevo adjusts this with the warehouse – without any manual intervention.
  4. Seamless Data Transformation: Hevo provides a visual Python-based interface where any data can be cleaned and enriched before loading the destination.
  5. Real-time Data: Hevo’s unique architecture loads the data from any source to the warehouse in near real-time. This ensures there is only a minimal lag in getting insights.
  6. Zero Data Loss: Hevo’s robust error handling module delivers accurate, consistent data to the warehouse without any data loss.

What more? Click here to avail a 14-day free trial of Hevo and experience hassle-free ETL, hands on. 

No-code Data Pipeline for your Data Warehouse