According to Gartner, The Future of Business Is Composable- There is an explosion of new companies, new products, and new features that will drive innovation and success. But, with more tools comes more complexity.

The number of business applications is exploding - driving unprecedented demand for ELT tools

This complexity has led to the rapid emergence of the Modern Data Stack and increasing demand for ELT tools. that centralize big data sets from disparate applications and create sanity from the chaos of applications sprawled across the organization.

But where do you get started when evaluating ELT tools? Which tools should you evaluate to power your data pipelines? How should you evaluate them? And what is the fastest way to get up to speed on your options?

We'll provide an overview of ELT, how you can evaluate solutions and the top 5 ELT tools you have to evaluate for your modern data stack in 2022.

Let's start from the top.


What is an ELT Tool?

In the article linked below, we outline:

  1. How ELT tools create value for users
  2. Who uses ELT tools
  3. What data ELT tools can extract
  4. The most common data sources for ELT tools
  5. And, the difference between ETL and ELT
What is ELT? [Extract, Load, Transform]
ELT tools sync raw data from applications across the enterprise into a data warehouse or data lake to power analytics and process automation.

At the highest level.

  • Definition of ELT: Extract, Load, Transform (ELT) tools offer no-code connectors that sync data from systems across the enterprise into a data warehouse or data lake to power data analytics and process automation. ELT tools load data in normalized schemas so that data analysts can then use SQL to build data models and transform the data into insights.
  • Value Proposition of ELT: Instead of writing python code to move data, ELT tools offer a no-code solution to sync data into data stores, so data teams can improve strategic decision making, reduce work for data teams, and streamline manual workloads. The source data is synced into the target system through the ELT process.
  • Users of ELT Tools: Data engineers and data analysts manage data processing infrastructure, including ELT systems, data flow orchestration, cloud data warehouses, and the tooling necessary to reliably transform data.
  • Common ELT Data Sources: ELT tools pull data from SaaS applications, files, databases, warehouses, event sources, and webhooks. The most common data sources are product databases, cloud platforms, CRM systems, ERP platforms, and HR applications.
  • What is the Difference Between ETL And ELT: ETL software (extract, transform, load) was popular when data needed to be transformed during the ETL process before it could fit into an analytics environment like a database. ELT tools approach data replication by moving raw data instead of including data transformation and aggregation while the data is in motion. Nowadays, most data teams use the ELT architecture instead of the legacy ETL architecture in their Modern Data Stack.

How should you select an ELT Tool?

In the article linked below, we outline the 5 main considerations when selecting an ELT tool.

​​How to Choose the Right ELT Tool
In this guide to choosing the right ELT tool, we outline the key considerations, questions to ask, and what to look for when evaluating options

To quickly recap, the five main considerations when choosing an ELT tool are:

  1. Connectors - Which connectors do they support? Do they extract data from the core systems you need to stand up your data stack? Can they handle ingestion from the long tail of your organization's applications to unlock easy data analysis?
  2. Roadmap - How fast can they build? Is the product user-friendly with a user interface that is actively being improved? What's their vision for growing with you as an organization? Are they responsive to requests? Do you need real-time capabilities? Are you handling unstructured data sets?
  3. Pricing - How much does the data integration platform cost? Does the pricing model align with your data profile?
  4. Support - How do you know your pipelines will be maintained? Do they have built-in scalability? How do they handle schema evolution when data types change? Who is 'on call' when things break? Do you have a direct line to someone that can solve your problem?
  5. Security - How does the company approach security, privacy, and compliance? Does it align with your company's particular considerations around data management?

Let's dig into the top 5 ELT tools on the market today.


The Top 5 ELT Tools

If you are ready to invest in an ELT solution, you need a starting point for evaluation. Below, we've outlined some of the key pros and cons of the top 5 ELT platforms on the market today.

1. Fivetran

Fivetran is the most established ELT tool on the market today. They were founded in 2012, they were one of the early players in the ELT market as the shift took place from ETL to ELT, and they provide a robust solution for core ELT connectors.

As a data integration tool, Fivetran is known to provide reliable cloud-hosted pipes for the largest databases and business applications (like Oracle and Salesforce) - connecting these data sources to the common data warehouses and data lakes. In 2021, Fivetran acquired HVR to double down on these high-volume connectors, and database syncing specifically.

In many scenarios, data teams that have access to budget (it's not cheap) will use Fivetran to stand up their modern data stack with core connectors to the largest applications within the enterprise. As needs expand, and long-tail business applications become important, it's common for data teams to augment Fivetran with additional ELT capabilities.

2. Stitch Data

Stitch Data played a similar role to Fivetran in the shift from ETL to ELT. In 2018, Stitch was acquired by Talend. This has led to changes in the team and divergence in the support model between Stitch-supported and community-supported connectors.

From a technical perspective, Stitch pioneered the modern open-source ETL tool model. With an open connector framework called Singer, Stitch provided the ability for community members to build and maintain their connectors. This community has developed, but in recent years, it has seen less investment than other open source communities in the space.

Stitch is a cost-effective solution for small data teams that don't want to spend much money on an ELT solution, but want a no-code vendor to provide core ELT connectors. As a tradeoff, when things go wrong, data teams end up working with the community to address issues.

3. Airbyte

Airbyte is a recent addition to the ELT landscape, and the company has raised significant capital very quickly. From a technical perspective, the Airbyte open source framework is not too dissimilar from the Singer framework developed by Stitch (overview of why).

For teams that want to deploy their infrastructure, build their connectors, and work with open source code directly, Airbyte is the most well-capitalized solution on the market. The connector catalog is on par with Singer, but support levels and investment are on the upswing while the Singer open source ecosystem sees less investment.

Airbyte recently released a cloud solution, which is new and competes on the common connectors you'll find from Fivetran, Stitch, and other core ELT solutions.

4. Matillion

Matillion is different from the other solutions on this list because it started as an ETL tool instead of an ELT tool. They've been around for a while.

Founded in 2011, Matillion has been solving data integration and data migration problems for large enterprises for over a decade. In addition to native transformations, one of the most unique aspects of Matllion is that the entire solution can be deployed on-premises, or in a cloud environment (even though the technology is not open source).

The enterprise flexibility, available transformation capabilities, and deployment model can make Matillion less approachable than the other tools on this list, but great to get started with large enterprise use cases.

5. Portable

Portable is a cloud-based solution focused on long-tail ELT connectors. As data teams aim to connect more and more applications to their data warehouse (Snowflake, Google BigQuery, Amazon Redshift, or PostgreSQL), they need to constantly search for a partner that can provide bespoke connectors.

Built from the realization that every ELT company was building the same 150 connectors, Portable has focused on building a platform on which new custom ETL connectors can be built on-demand for clients in hours or days.

So, even in scenarios where you are using Fivetran, Stitch, Airbyte, or Matillion, Portable is the perfect solution to provide a no-code integration workflow syncing data from bespoke business applications into your data warehouse quickly. It's extremely simple to get started.

Even though Portable is the most recent addition to the ELT landscape on this list - with over 275+ API data sources - Portable has more cloud-hosted, no-code connectors than every other company on this list.


Want to learn more about Portable? Book time for a discussion or a demo directly on my calendar

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