ELT/ETL: discover the most powerful tools on the market

Access to ever-increasing amounts of data has made ELT one of the most fundamental processes for analyzing and storing data.

Until recently, the ETL process (in Portuguese, extract, transform and load) was the most popular method for carrying out this task. However, modern companies are gradually switching to the ELT approach (in Portuguese, extract, load and transform), a much more agile, scalable, flexible and economical alternative .

And do you know the secret to an efficient ELT process?

The answer is simple: the right tools .

Follow us to learn more about the differences between ETL and ELT and discover which are the most powerful tools on the market for integrating and optimizing your company's data.

What is ETL?

ETL is the traditional data transformation approach, in which the following functions are performed, in this order:

1. extracting data from different sources.
2. data transformation for use.
3. loading data into a data warehouse structure.

ETL has always been, and continues to be, a fundamental process in data operations as it enables the integration, organization and centralization of information from different sources in a single location, a data warehouse .

However, with the emergence of cloud data warehouses, ELT emerged as an alternative to traditional ETL. This new approach offers some innovative benefits, including:

• the possibility of storing large volumes of data in a scalable way in the cloud (cloud DW)
• the use of cloud dw with data transformation engines
• ease of use in a single programming language

There is no doubt, therefore, that ELT has arrived to revolutionize ETL. Shall we understand better?

What is ELT?

Unlike ETL, the ELT process follows the following steps:

1. extraction: collecting and extracting raw data from one or multiple sources.
2. loading: loading collected data into a data warehouse.
3. transformation: transformation of raw data into modeled data.

It can be seen that there is a phase inversion!

In other words, the extract-transform-load (ETL) process is reformulated into an extract-load-transform (ELT) process. With this change, the transformation stage now takes center stage and operates through models written in SQL that are easy to maintain and widely understood.

Furthermore, the inversion of phases in the ELT process allows data transformation to be conducted by professionals with a business background, such as analytics engineers or data analysts. Therefore, it also facilitates the application of business intelligence and data analysis by multidisciplinary teams, reducing dependence on highly technical professionals, such as developers and data engineers.

To top it off, ELT, also known as “automated ELT” or “automated data integration” , is part of the modern data platform (or modern data stack ), a cheaper and simpler modern approach to configuring and handling large volumes of data.

Want to learn more about the differences between ETL and ELT, and why modern data teams are opting for ELT? Access our full content .

ETL/ELT tools in 2021

Choosing the right ETL/ELT tool for your business is the first step towards efficient data transformation in your business.

With that in mind, we will present the 12 most powerful ETL/ELT tools on the market.

To facilitate understanding, we organize these technologies in order based on the processes followed by the ETL/ELT flows ( extraction, loading and transformation ) and the sub-steps that accompany the process, such as collection at the beginning of everything and data storage, after charging.

Let's go?

Data collect

Snowplow

Snowplow is one of the best data extraction platforms , which allows any company to collect its own granular data, in its own cloud account, giving data scientists and analysts more freedom .

Additionally, your data is available in real time and can be loaded into any data warehouse and used to power BI tools , custom reports or machine learning models.

Segment

Segment is the platform that collects , cleans and controls customer data (or CDP, customer data platform), to send it to storage.

This ETL/ELT tool provides an API with native library sources translated into all languages , and supports the collection and routing of customer data to over 180 different database tools and services . Finally, it guides this raw data collected from customers to data warehouses for exploration and advanced analysis.

Data extraction and loading

Stitch

Stitch is another powerful ETL/ELT service that integrates data from multiple sources into a central data warehouse . However, this is a platform focused on developers , who can receive data from more than 120 cloud sources through WebHooks and an API.

Furthermore, other differentiators are the fact that it offers self-service ELT and also automated data pipelines, making the process simpler.

Fivetran

Fivetran is a cloud-based ELT solution that helps integrate data with the main data warehouse platforms available on the market.

Among the numerous benefits of Fivetran are:

• the wide variety of data sources - around 90 possible SaaS sources.
• the ability to integrate with other platforms.

County

Kondado is a Brazilian web platform , focused on analytics , connected to more than 50 tools and databases to perform data integration and modeling.

For example, with Kondado, it is possible to centralize information taken from various tools and databases within an organization, into a data warehouse . In other words, the platform operates as a bridge between an organization's tools and its analytical database .

Do you know what the biggest advantage is?

You can do all this without having to write a single line of code .

Data storage

Oracle

Oracle Cloud Infrastructure offers low-cost cloud storage and the ability to operate at the highest performance requirements . It serves storage workloads through local on-demand storage, offering real-time elasticity , whether object, file, block, or archive.

Another benefit is that customers can use their storage gateway and data transfer service to securely transfer their data to the cloud .

Google BigQuery

Google BigQuery is a serverless, multi-cloud data storage web service. It is highly scalable and cost-effective and is designed to streamline your business. As?

This ELT service can analyze billions of rows of data, using SQL-like syntax, at incredibly fast speed and without any operational overhead.

PostgreSQL

PostgreSQL is a complete open source object relational database system , considered one of the most well developed and mature with its more than 20 years on the market.

But it's not just a simple relational database. It also serves as a time series database and even as an efficient and low-cost data storage solution.

What's more: it can be integrated with various analysis tools, be widely compatible, have low cost and high performance, PostgreSQL is one of the most used options among companies.

Snowflake

Snowflake is another cloud data warehouse service , which automates data warehouse administration and maintenance and supports transformation during (ETL) or after loading (ELT).

It works with a wide variety of data integration tools and allows storage to be done independently, allowing customers to contract space and maintenance separately.

Data transformation

dbt

dbt is a data orchestrator . But what does this mean?

We explain: among ELT services, dbt is responsible for carrying out all the most common transformations necessary to build a DW.

One of the main functions that differentiates dbt from the various ETL/ELT tools is the possibility of creating codes in SQL. This gives data engineers complete independence to operationalize complex ELT processes, also encouraging teamwork between technical professionals and analysts, all directly within a data warehouse.

In fact, it is in partnership with dbt , this powerful tool, that Indicium 's data team carries out the most complete ELT projects, implementing the best practices in analytics engineering .

Spark

Apache Spark is an open-source data processing and analysis engine used to work with large-scale datasets . In it, you can do all the searches, joins, cleaning, data transformation and enrichment.

One of Spark's differentiators is the availability of higher-level libraries , including support for SQL queries, data streaming, machine learning, and graph processing, which increase developer productivity and can be combined to create complex workflows.

Dram

Dremio also works with data storage, but its main function is to be a “ data lake manager ”, helping data teams overcome major problems in structuring data lakes, with functions such as:

• offloading large volumes of data.
• unifying data from different sources.
• separation of physical modeling and logical modeling.
• Upload files for exploration and enrichment.

And what's more, Dremio operationalizes your data lake storage and accelerates your analytical processes with a high-performance, high-efficiency query service.

Invest in ELT

Now that you know some of the most powerful tools on the market, it's time to start exploring them.

And if you need help implementing ELT in your business, count on the help of experts in the most advanced technologies : Indicium .

Get in touch to talk about your ELT project today by clicking here!