Businesses today are trying all kinds of ways to get insights from their data faster. Not only is there a huge and growing amount of data but it also moves incredibly fast and comes from many different sources. There are more siloed data sources and systems than ever before – and you probably don’t have enough resources or build connectors for all your data sources.
Hence finding the best ETL tool that suits your business becomes a tedious task for the data engineers. Eventually, they should ensure that their ETL tool works without a hitch and data is effectively accessible to data analysts and business groups.
In this post, we will explore the top 10 ELT tools and aid your decision-making process. This blog about ELT tools is a relatively detailed one. Use the navigation links below to instantly traverse to the required point.
Table of Contents
- What is ELT?
- Why do we use ELT tools?
- Cloud-native and Open Source ELT Tools
- 10 Best ELT Tools
What is ELT?
In this process, the large amount of unstructured data is extracted from a source system and loaded onto a target system to be transformed later, as needed. Then the Data is made available to business intelligence systems, this process leverages data warehousing to do basic data transformations, such as data validation or removal of duplicated data.
ELT is a more up to date process that has not arrived at its maximum capacity contrasted with its more established sister, ETL.
Why Do We Use ELT Tools?
The ELT approach empowers quicker execution than the ETL process, though the data is messy once it is moved. ELT decouples the transformation and load stages, ensuring that a coding error (or other error in the transformation stage) does not halt the migration effort.
ELT also avoids server scaling issues by using the processing power and size of the data warehouse to enable transformation (or scalable computing) on a large scale. ELT works with cloud data warehouse solutions to support various data types such as structured, unstructured, semi-structured, and raw data types.
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As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.
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What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, and custom ingestion/loading schedules.
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Cloud-Native ELT Tools and Open Source ELT Tools
Paid ELT Tools
If you have high data analytics ambitions, you need a modern data stack — connectors, cloud warehouse, and BI tools. Paid solutions are a great choice especially when you want to minimize the TCOs of equipment and maintenance costs. Today there are dozens of commercial SaaS ELT tools available. They are offering real-time streaming, monitoring and alerts, intelligent schema detection, and more.
Free ELT Tools
Like all fields of software development and infrastructure, ELT has seen its own surge of open source projects that are free to download and are licensed under an open-source license.
Open source ELT tools are a cost-effective alternative to commercial solutions. They are a great fit for smaller projects that either lack the time and resources in-house to build a custom ELT solution — or the funding to purchase one.
Now that we know what an ELT tool is, let’s have a peek at the list of top ELT tools and compare them.
Top 10 ELT Tools
Top 10 ELT Tools Comparison
Hevo Data, a No-code Data Pipeline helps you to replicate data from any data source with zero maintenance. You can get started with Hevo’s 14-day Free Trial and instantly move data from 150+ pre-built integrations comprising a wide range of SaaS apps and databases. Using Hevo, you can precisely control pipeline schedules down to the minute.Get Started with Hevo for Free
Setting up data pipelines with Hevo is a simple 3-step process by just selecting the data source, providing valid credentials, and choosing the destination.
Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Check out what makes Hevo amazing:
- Near Real-Time Replication -: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
- In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
- Monitoring and Observability-: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
- Reliability at Scale -: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
- 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
Hevo Data provides Transparent Pricing to bring complete visibility to your ETL spend. You can also choose a plan based on your business needs.
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Luigi is a Python library that provides a framework for building complex data pipelines. Luigi was built at Spotify. The purpose of Luigi is to allow you to automate and chain batch processes.
- Supports dumping data to and from databases
- Supports running machine learning algorithms.
- Luigi features reliable throughput with real-time elasticity, scalable to millions of events per month.
- Ability to build up long-running pipelines that comprise thousands of tasks.
- Support for running Python MapReduce jobs in Hadoop, Hive, as well as Pig.
- Ships with file system abstractions for HDFS and local files to ensure that the system can handle failures and that your data pipeline will not crash in a state containing partial data.
- The server comes with a Web UI for workflow management and visualization of the dependency graph of the workflow.
- It handles dependency resolution
- Command-line integration.
- Stores the state of the ELT pipeline in Elasticsearch.
Best-suited use case
Luigi is best suited for organizations that run thousands of tasks every day, and they need to organize it in complex dependency graphs. It’s especially suited for building complex and ever-changing ELT pipelines.
- Steep learning curve. You would need you to invest heavily in engineering resources that can build and maintain this infrastructure.
- Hard to test tasks using the API.
- If the scheduler is busy or other concurrent users are using the UI, the UI suffers from disappointingly sluggish performance.
Luigi is free and open source.
Blendo is known as one of the best ELT tools enabling customers to centralize all of their different datasets and data sources into a central location. They are in the business of building new connectors and maintaining the ones already created.
Over the years, they’ve grown to over 40+ integrations. They provide a fast way to replicate your application, databases, events, and files into the fully managed and elastic cloud warehouses such as BigQuery, and Redshift.
- Fully managed data pipelines as a service
- Limited maintenance and configuration
- Automated schema migrations
- 40+ connectors and counting
Best-suited use case
It is a good choice for businesses that want to move data from Facebook Ads, Google Ads, Google Analytics, Hubspot, LinkedIn Ads, Mailchimp, MySQL, Salesforce, Shopify, and Stripe to Amazon Redshift, Google BigQuery, Microsoft SQL Server, Snowflake, PostgreSQL, and Panoply.
- Blendo works as a extract and load kind of a set up. They do not provide a way to transform the data before or after loading to the warehouse. This becomes limiting when your ETL use cases start to evolve.
Prices start at $150 for the starter package which has standard connectors and $500 for the advanced package which has over 150 pipelines.
Matillion is one of the best ELT Tools that is built specifically for Amazon Redshift, Azure Synapse, Google BigQuery, and Snowflake. Matillion has an ELT architecture. It sits between your raw data sources (internal, external, and third-party data) and your BI and Analytics tools.
Matillion ELT takes away the compute-intensive activity of loading data from your on-premise server that is perhaps already under pressure with its regular transaction-handling role and instead leaves this process to the data warehouses that tend to have an infinite amount of parallel processing resources.
- Pay-as-you-go model with no long term financial commitments.
- Scalable built to take advantage of the power and features of your data warehouse.
- Makes complex tasks simple with an intuitive UI and approach to data transformation.
- Automated data workflows.
- Drag-and-drop browser-based UI so you can build your ELT jobs in minutes.
Best-suited use case
If you’re using Amazon Simple Storage Service (S3), Amazon Redshift, Azure Synapse, Google BigQuery, or Snowflake for your data Warehousing needs, then Matillion is a good choice for your use case. However, keep in mind that Matillion doesn’t support ELT load jobs to other Data Warehouses—it is designed specifically for those solutions.
- Learning curve – understanding and implementing complex features becomes challenging for new development teams.
- You can sometimes encounter validation failure in scheduled jobs for no discernable reason.
- Clustering is not supported which means large load jobs can take up a long time to process or even lead to OOM errors.
- Integration with version control systems is a complex undertaking.
Now Matillion is making their ELT platform available starting at $1.37 per hour which translates to $12K annually assuming 24/7 usage. For larger teams and higher performance production workloads, there is a plan that starts at $5.48 per hour or $48K annually. They also offer a free 14-day trial.
The Talend cloud data integration tool is known as one of the best ELT tools. It is a modern big data and cloud integration software to connect, extract, and transform any data across the cloud and on-premises. They are enabling companies to harness the power of their enterprise information and to turn that data into insights so that they can get ahead.
Talend provides a data integration platform natively designed for the new Big Data and the cloud-centric world that empowers companies to immediately turn data into business insights.
- A subscription-based data management platform.
- Variety of connectors to various data sources.
- Management and monitoring capabilities.
- Log collection and display.
- Easily deployable in a cloud environment.
- Data can be loaded into your data lakes and warehouses without formatting which makes the ingestion speed much quicker.
- A healthy online community that can assist you with any technical support issue.
- Connectors for Snowflake, Amazon Redshift, Azure Data Lake Storage Gen2, Azure SQL Data Warehouse, Databricks Delta Lake, Google BigQuery, Oracle, Teradata, Microsoft SQL Server, SaaS, Packaged Apps, SMTP, FTP/SFTP, LDAP, and more.
Best-suited use case
If you have your data in on-premises data warehouses, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, SAP, Salesforce, Oracle — Talend connectors support all these use cases and more. Talend is full of features and built-in components.
- The job editor is quite heavy and it can stall during heavy tasks.
Talend costs $1,170 USD/user or $12,000 USD/user where you save 15%. They also offer a free-to-download open-source ELT tool, Talend Open Studio, with limited features. You can use the free 14-day trial that includes all features, after which point you can upgrade your trial into a monthly or annual subscription.
Streamsets is a cloud-first enterprise ELT tool for extracting data from SaaS applications and databases into data warehouses and data lakes for last-mile analysis. Enterprises use Streamsets to consolidate dozens of data sources for analysis.
- Easy self-serve model for replicating data from more than 100 applications and databases.
- Highly extensible – customers can add new data sources.
- Available in the AWS store.
- Ability to replicate, merge, as well as segment and route data.
- HIPAA, GDPR, SOC 2 compliant.
Best-suited use case
If you have IoT edge devices, and you need a lightweight execution agent that runs pipelines on edge devices then you will find that SteamSets is an ideal ELT for this use case. StreamSets is one of the many ELT Tools that integrate seamlessly with the old Java platform.
- It is not a good fit for very low latency use cases as it is much better suited for batch data processing.
- It has fewer connectors compared to most tools in this list although the community regularly updates it with new connectors.
StreamSets is one of the few ELT Tools that are free and open source.
Etleap is a next-generation ELT solution that has integrations with 50+ data sources.
- Etleap has an intuitive graphic interface that allows you to easily orchestrate and schedule data pipelines using Etleap’s workflow engine.
- It can deliver this data to many types of systems within milliseconds. After your data lands on disk, Etleap makes it easy to filter, aggregate, load, transform and enrich it.
- Connectors can be built rapidly without coding skills meaning your data engineers get to immediately focus on better understanding your customers and growing your business.
- Data pipeline monitoring is made available through the dashboard.
- Etleap can ingest data from a wide variety of sources –
- Change data (CDC) from enterprise databases
- Log files
- Message queries
- Simple File Storage
- ERP systems, etc.
Best-suited use case
If you are a business that generates and collects large amounts of data, and you find yourself needing to use a low-maintenance, fully managed ELT solution, then ETLeap is a good fit for you.
- It has limited integrations relative to most of the ELT tools reviewed in this list.
The company does not disclose its pricing structure. To purchase the solution you need to have a conversation with a sales engineer. A free personalized trial is available but you will still need to request for it.
Apache Airflow was developed by the engineering team at Airbnb and then later open-sourced to Apache. Airflow is typically used to create jobs, scheduling said jobs, and monitoring your ELT workflows/pipelines.
An Airflow workflow is a sequence of tasks defined using the Python programming language. These tasks can be initiated on a schedule or even by an event. The pipeline can also send reports on the status of your pipelines using email and is one of the many ELT tools to do so. You can read more about Airflow here.
- Define Airflow pipelines in Python.
- Execute, schedule, and distribute tasks across worker nodes.
- Logging feature with a detailed view of present and past runs.
- Extensible through plugins.
- Used by more than 200 companies including – Yahoo! AirBnB, PayPal, Intel, Stripe, and Yahoo!
Best-suited use case
Airflow is one of the ELT Tools that are best suited for orchestrating complex data processing pipelines. If you require a custom data pipeline, then you can use Python to programmatically define your own custom operators, executors, monitors, etc for your ELT pipeline. You can monitor all ELT processes in the user-friendly UI that displays complex workflows as SVG images.
The solution is also highly scalable. You can use it in a single node and it is also possible to have a cluster of workers.
- A steep learning curve given the extensive UI and the non-trivial process of creating new connectors.
- If your use case has many long-running tasks then you might experience that the Airflow scheduler loop introduces significant latency. To avoid this you can use a Kubernetes executor to scale it to run thousands of concurrent workflows.
- The centralized nature of the Airflow scheduler introduces a single point of failure for the system.
- There is no support for dependency resolution and so tasks cannot communicate with each other.
- Creating custom hooks and operators adds additional operational overhead and takes away your focus from your core business outcomes.
Airflow is free and open source, licensed under the Apache License 2.0.
Apache Kafka was created by LinkedIn and is now an open-source project mainly maintained by Confluent under the Apache stewardship. Kafka allows you to decouple your data streams and your systems. So your source systems will have your data in Apache Kafka and your target systems will source their data from Kafka in an ELT fashion.
You can have any data stream you like e.g. website events, pricing data, financial transactions, user interactions, etc. Once the data is in Kafka, you can put it in any system you like e.g similar to several other ELT Tools.
- Analytics systems
- Email systems
- Audit systems
Many companies are using Apache Kafka as their backbone. For example
- Netflix uses Kafka to apply recommendations in real-time while watching TV shows.
- Uber uses Kafka to gather user, taxi, and trip data in real-time to compute and forecast demand, and also to compute surge pricing.
- LinkedIn uses Kafka to collect user interactions to make better connection recommendations in real-time.
- A distributed, resilient, and fault-tolerant architecture.
- Real-time stream processing, activity tracking, and application logs gathering.
- Horizontal scalability-LinkedIn has proven that it can scale to millions of messages per second.
- Extremely high performance (latency of less than 10ms) – real-time.
- Used by 2000+ firms, 35% of the Fortune 500 such as LinkedIn, Airbnb, Netflix, Uber, Walmart, etc.
Best-suited use case
Kafka works well with systems that have data streams to process. Kafka enables those systems to aggregate, transform & load into data stores as they occur. For example, Kafka is great for Log aggregation. You can use it to collect physical log files off servers and push those to a central repository (a data warehouse or file server) for processing in a classic ELT fashion.
- Kafka is missing a complete set of management and monitoring ELT tools which is a deal-breaker for some organizations.
- Lack of pace in the development of new features.
- Zero data loss is still not guaranteed.
- Data retention is expensive because the data is often duplicated.
- A high number of brokers, partitions, and replications adds some serious complexity in the system and it often takes a while for developers to wrap their heads around all the pieces in the puzzle
Kafka is one of the free and open-source tools, licensed under the Apache License 2.0.
Apache NiFi is a project that was initially developed by the US National Security Agency (NSA) to automate the flow of data between software systems. Similar to Airflow, NiFi is based on a concept called flow-based programming (FBP).
NiFi performs a combination of Extraction, Loading, and Transformations between systems. It can operate within clusters and can be used to create both ELT and ETL workflows.
- Easy to use data flow pipelines that send, receive, transfer, filter, and move data.
- Flow-based programming and a minimalist user interface.
- GUI can be customized based on specific needs.
- Visibility through end to end data flow monitoring.
- Pluggable, multi-tenant security.
- Highly extensible and you can build your own processors.
- It supports HTTPS, SSL, SSH, multi-tenant authorization, etc.
- Automated pipelines that require minimal manual intervention to operate.
- It has a version control system for data-flows.
Best-suited use case
NiFi is suited for processing both streaming data and batch load jobs. If you are looking for an ELT tool that has a minimalist UI, is versatile, and performs decently, then you should check out this tool.
- Lack of real-time monitoring tools.
- Failover is not supported by default.
- Horizontal scaling is hard to implement. Therefore, the more pragmatic approach here is to scale vertically through the use of larger instances.
- Resilience against server problems is not supported internally.
NiFi is free and open-source, licensed under the Apache License 2.0.
This blog talks about the best tools available at your disposal today. It gives a brief insight into the features, pricing, specific use cases, and drawbacks of each tool to help you make an educated decision. Looking for a reliable fully automated ELT tool, then Hevo Data is the right choice for you!
You can also read how Hevo’s Inflight Transformation allows data teams to perform lightweight data transformation inflight.
Hevo being a No-code ELT Pipeline will leverage the power & speed of cloud to quickly integrate data from all your sources, Load it to a destination of your choice and Transform it into an analysis Ready form. Hevo comes with Automatic Schema Management, Real-Time Monitoring & Alerts, Custom Query builder, etc. that promise that all your ELT needs are met.
Want to take Hevo for a spin? Sign up for a free 14-day Free Trial and experience the feature-rich Hevo suite first hand.