Top 6 ELT Tools to Consider in 2023
April 1, 2022
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ELT (Extract, Load, and Transform) tools are data integration tools that extract data and load it into a data lake for transformation and analysis.
ELT tools are becoming increasingly popular as they can handle massive volumes of unstructured data, non-relational databases, and large data sets requiring powerful parallel processing.
Using ELT also means that you can come up with analytics use cases at any time, rather than mapping and designing them beforehand.
However, ELT tools are relatively new, compared to the more established ETL tools. This article covers the top ELT tools that offer you more flexibility when working with large data sets.
Here are the six most popular ELT tools in 2023:
Airbyte is an open-source ELT tool for modern data teams. It extracts raw data from sources such as databases and web applications with pre-built or custom connectors within minutes and loads them into the destination repositories with the Airbyte UI or APIs.
Each connector runs as a Docker container and allows you to use your language of choice. You and schedule pipeline updates.
What are the main capabilities of Airbyte?
- Airbyte provides a connector development kit (CDK) with 140+ pre-built connectors, simplifying the building and maintenance of connectors with a low-code/no-code environment.
- You can monitor individual pipelines with real-time error logs. You can also set up real-time alerts and notifications with webhook.
- You’re also in complete control of the pipelines, and you can debug them as per your needs.
- Airbyte integrates with your data stack — Kubernetes, Airflow, dbt, and more modern technologies. You can also add custom transformations using dbt.
Airbyte Docs | Airbyte API Docs | Changelog
StreamSets is a continuous big data loading platform that builds resilient data pipelines and manages data modification. It helps build smart data pipelines that cater to all data engineering life cycle needs.
These pipelines facilitate data migration across hybrid and multi-cloud environments without requiring rewrites. StreamSets also enhances the ELT process by allowing data pipeline streaming for transparent data movement and management.
What are the main capabilities of StreamSets?
- StreamSets offers a single UI to design, deploy, monitor, and manage smart data pipelines. It’s scalable in any cloud or hybrid environment.
- StreamSets provides complete visibility and control over all pipelines and their operations. These pipelines also support data replication, merging, segmenting, and routing.
- You can build smart pipelines that provide continuous data flow even when structures and schemas change.
StreamSets Documentation | StreamSets Setup | StreamSets DataOps Blog
Blendo provides ready-to-use pipelines for the extraction and loading processes of ELT. It lets you speed up data analysis by extracting raw data from disparate sources in minutes and loading it into a data warehouse.
Blendo easily integrates with data repositories such as Amazon Redshift, Google BigQuery, SQL Server, Snowflake, and Panoply as well as BI and analysis tools such as Chartio, Google Data Studio, Tableau, and Sisense.
Blendo also offers a free 14-day trial to help you get started.
What are the main capabilities of Blendo?
- You can use ready-made connectors to connect to any data source, without writing any scripts or complex integration workflows.
- Blendo reduces data integration workload by automating data migrations, with minimal maintenance and configurations.
- Blendo provides analytics-ready and pre-defined data models for faster data exploration.
Blendo Guides | Blendo Knowledge Base | Blendo Blog
Hevo offers no-code, bi-directional data pipelines that streamline and automate data flows. It offers a user-friendly interface for data replication, cleansing, and preparation.
Hevo modernizes ELT data transformations using the ‘models and workflows’ approach — models enable data transformations, and workflows enhance these models by merging them for deeper analysis.
What are the main capabilities of Hevo?
- Hevo supports 100+ ready-to-use integrations across databases, SaaS apps, cloud platforms, and streaming services.
- You can automate data preparation with Hevo’s schema detection for mapping incoming data to the right target schema.
- Hevo empowers you to build scalable data pipelines that can handle millions of records per minute.
- You can also modify, monitor, and observe the flow of data in real-time.
- Hevo provides secure and fault-tolerant services by ensuring that no data gets lost or corrupted. The corrupted data set gets corrected separately without affecting other workflows.
Hevo Documentation | Hevo API Docs | Hevo Blog
Fivetran is a fully-automated data ingestion and replication tool for extracting and loading data. It lets you build no-maintenance data pipelines that quickly deploy to ingest and replicate data into warehouses.
Fivetran has also incorporated data transformations into a unified ELT platform to help data analysts with data preparation, analytics, and reporting.
What are the main capabilities of Fivetran?
- Fivetran offers a complete data integration package. It incorporates data transformations and provides built-in version-control, documenting, and testing.
- Fivetran provides data monitoring features that raise alerts and allow you to redo corrupt processes. It also ensures the error-free running of all ELT processes.
- Fivetran enables database replication, and advanced analytics for marketing, sales, finance, and customer success.
Fivetran Documentation | Fivetran Status
Stitch is an open-source, cloud-first ELT data integration platform owned by Talend. It’s a self-service ELT tool that automates data pipelines for rapid data flow.
It offers a simple user interface that enables single-click data ingestion, empowering analysts and business users to perform data transformations and analytics.
What are the main capabilities of Stitch?
- Stitch enables data type conversion and schemas management by automatically adapting incoming data to its target schema.
- You can schedule the loading of data into schemas so it can be analyzed when required.
- Stitch users can quickly adapt to using the tool with its easy-to-understand processes. Hence, Stitch does not require its users to be technically advanced.
Stitch Documentation | Stitch Trial
How to evaluate ELT tools
The most common requirement from ELT tools is to reduce or eliminate the technical burden on data teams by automating several processes, such as data extraction, ingestion, and loading. ELT tools also let you integrate analytics tools directly with the target warehouses and lakes.
Here are some of the other factors you should consider when evaluating ELT tools:
- The data types and sources that the ELT tool can handle
- The use cases where ETL isn’t required and ELT is a better option
- The cost and time savings in data extracting, loading, and processing
- The amount of effort and resources saved from transforming data in the target warehouses or lakes
FAQs about ELT tools
What are ELT tools?
ELT tools are data integration platforms that extract raw data and transfer it to target systems such as data lakes. These systems are used to handle data transformation processes.
Also, check out the Top 5 ETL tools to consider in 2023.
ELT tools vs. ETL tools: What’s the difference?
The main difference is in the sequence of events.
For example, ETL tools transform raw data and load it into warehouses. Meanwhile, ELT tools extract and load data into lakes, offering more flexibility in handling and organizing data sets.
Using ELT tools also means that you don’t have to use scripting languages like Python to write transformation scripts, as SQL works for transformation scripts inside warehouses. These tools empower analysts and business users to build and extract value from analytics use cases.
Also check out ETL vs. ELT: Which data integration process is ideal?.
Why are ELT tools used?
ELT tools are ideal for handling large data sets and unstructured and non-relational data.
However, they’re not helpful when the raw data requires extensive cleansing before loading. They are better for cases where the original data is simple but present in large volumes.
What are the procedures involved in ELT?
- Data extraction from various sources
- Data ingestion into data pipelines
- Data replication for loading it into the target system
- Data transformation inside the target repository
ELT tools: Related reads
- ETL vs. ELT: Which data integration process is ideal?
- Data transformation: What, how, and why it’s needed
- Data ingestion vs. data integration: How are they different?
- The future of the modern data stack in 2023
- The building blocks of a modern data platform
Related deep dives on popular data tools
- 7 popular open-source ETL tools
- Top 5 ETL tools to consider in 2023
- 9 best data discovery tools
- 5 popular open-source data catalog tools to consider in 2023
- 7 popular open-source data governance tools to consider in 2023
- 12 popular observability tools in 2023
- 10 popular transformation tools in 2023
Photo by Yagyaansh Khaneja
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