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Best ETL Tools for Data Warehouse

Best ETL Tools for Data WarehousingFinding the ETL tool that fits your use case like a glove can be hard. This article aims to guide you with broad classification and use cases of different ETL tools. The blog will also provide you with a framework on how to go about choosing the right ETL tool for you. By the end, you should be able to pick the best ETL tool as per your use case. 

Before you dive into understanding what the top ETL Tools in the market today are, it is important to briefly understand the ETL process itself. This will set you up better to appreciate the value provided by different ETL tools. 

What is ETL?

ETL is the process of moving raw data from one or more sources into a destination data warehouse in a more useful form. This is an essential process in making the data analysis-ready in order to avail business intelligence.

For example: In order to derive deep insights on your marketing metrics, you would have to move raw events from Google Analytics, Google Ads, Salesforce, and other applications used by your marketing teams and load it in a SQL-like format into a data warehouse like Amazon Redshift. 

ETL simply stands for – Extract, Transform and Load. Often, the process entails the following: Data is first Extracted from the source and maintained in a staging area. While in the staging area, depending on the use case of your business, the data is then Transformed into a format that’s more useful for analysis and more appropriate for the destination warehouse schema. It is then Loaded into the destination warehouse.

What are ETL tools?

ETL tools are applications/platforms that enable users to execute ETL processes.  In simple terms, these tools help businesses move data from one or many disparate data sources to a destination. These help in making the data both digestible and accessible (and in turn analysis-ready) in the desired location – often a data warehouse. 

Before we jump to discussing the best ETL tool for your use case, let us understand the broad classification of the same. 

Based on the needs/use case for integrating data, ETL tools can be broadly put into the following categories.

Types of ETL Tools for Data Warehouse:

  • Batch ETL
  • Real-Time ETL
  • Cloud-based ETL

Let us look at the use case and the need for each one in detail. Towards the end, you would have the right lens to pick the best ETL tool for your business.  

Batch ETL Tools

In the early days, bandwidth and computing power was very expensive and/or required rationing. The ETL process required (and still does, to some extent) significant portions of said resources. This led to a trade-off between performing daily business analytics and performing ETL. 

The resulting compromise would be to run the ETL in batches during business off-hours, which became known as the “batch window”. Even now, after decades of development and evolution in computing and communication, the Batch ETL process is still used.

You’ll find this practised in situations where:

  • Legacy systems are still used for vital business processes
  • The systems that are used to host the data source and/or data warehouse are on-site
  • The activity of data extraction would interrupt the functioning of business transaction systems
  • The volume of data is so large that it requires privileged use of available resources for efficient processing

This method is also used by many who may not be in such specific scenarios due to its benefits including simplicity, limited user involvement, and improved data quality.

What are some of the best Batch ETL Tools?

The following tools are some of the best ETL tools for batch data replication.

Real-Time ETL Tools

Business intelligence is only as good as the data it’s based on, and, increasingly, decisions need to be based on what’s going on now. As such, the real-time availability of data is becoming a more fundamental issue. Fortunately, advancements in technology allow companies to get a full snapshot of their business activities in real-time (or near-real-time). This is done using a process called data streaming.

Event records are able to be moved immediately to the relevant destinations, almost regardless of volume, allowing analysts to glean useful information from the most up-to-date data sets. Some of the best ETL applications today are able to take full advantage of existing technologies and infrastructure to construct highly efficient data pipelines that move data from multiple data sources to the data warehouse in real-time. 

What are some of the best Real-time ETL Tools?

The following tools are some of the best ETL tools with support for real-time data replication.

Cloud-Based ETL Tools

Increasingly, more applications are moving to the cloud. Naturally, users are shifting their resources to cloud-based services. Services like Google Analytics, HubSpot, Salesforce, Zendesk, Shopify and more are being used by companies to operate.

Despite this change, companies still need to move their data from these cloud-based applications to their data warehouses for analysis and insights. 

Notably, companies like Google and Amazon are providing cloud-based solutions for data warehousing – Google BigQuery and Amazon Redshift.

These services result in significant cost savings for organisations as they are no longer required to spend large amounts of capital on building and maintaining architecture for their data warehousing needs. Companies also benefit from the colossal computing power provided by the vast IT infrastructure of behemoths like Google and Amazon. For a relatively small expense, customers can run queries on terabytes of data and receive results in mere seconds. 

A natural consequence of this trend is that companies have developed Data Integration tools that provide robust ETL from various cloud-based applications to the warehouse.

What are some of the top Cloud-based ETL Tools?

The following tools are some of the best cloud-based ETL tools for data replication.

How to choose the right ETL Solution for your organisation?

Naturally, in order to decide what ETL solution may be best for you, you would have to review your organisation’s own use cases first.

If you are responsible for the data infrastructure for a long-established institution that has systems based on, or integrated with, legacy software, or maintain systems that require batch processing of your data, then chances are you may be wedded to an ETL solution that is compatible with the systems that your organization has been using so far. If, on the other hand, your organisation is taking steps to migrate some or all of their OLTP and OLAP assets to more modern solutions then read on.

If the major concern in your data integration needs is:

  1. Real-time availability of data
  2. Ability to move data from cloud-based platforms

A hassle-free, modern data integration platform like Hevo might suit your needs. Hevo brings data from 100s of disparate data sources into the Warehouse in Real-time – without writing a single line of code. 

Why Use Hevo as your ETL Tool?

  • Easy Setup and Highly Intuitive User Interface – Hevo has a minimal learning curve and can be set up in minutes. Once the user has quickly configured and connected both the data source and the destination warehouse, Hevo moves data in real-time
  • Fully Managed– No coding nor pipeline maintenance is required by your team
  • Unlimited Integrations – Hevo can provide connectivity to numerous cloud-based and on-site assets. Check out the complete list here: hevodata.com/integrations
  • Automatic Schema Mapping – Hevo automatically detects the schema of the incoming data and maps it to the destination schema. This feature frees you from the tedious job of manually configuring schema
  • Effortless Data Transformations: Hevo provides a simple Python interface to clean, transform and enrich any data before moving it to the warehouse. Read more on Hevo’s Transformations here.

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