Azure ETL Tools: Top Picks for Azure Blob Storage Unveiled

Unlocking the full potential of data stored in Azure Blob Storage requires leveraging robust Azure ETL tools. These tools seamlessly integrate, process, and manage data pipelines, streamlining workflows within the Azure ecosystem.

As Azure Blob Storage is a cornerstone of cloud-based data storage, selecting the right Azure ETL tool is crucial. These tools automate data ingestion, orchestrate transformations, and optimize data management processes.

Join us as we explore the features, functionalities, and real-world applications of leading Azure ETL tools tailored for Azure Blob Storage. Whether you're a data engineer, analyst, or business decision-maker, this exploration equips you to unlock actionable insights from your Azure Blob Storage data."

What is ETL?

ETL (Extract, Transform, Load) is a fundamental process in data management, pivotal for extracting, transforming, and loading data from diverse sources into a target database or data warehouse. This process ensures that raw data is refined and structured to align with specific formats or schemas, optimizing it for analysis and reporting purposes. ETL plays a vital role in data integration and is essential for consolidating information from various systems into a unified repository.

ETL processes are characterized by their batch processing nature, handling large volumes of data in scheduled intervals. This methodology ensures efficient data handling, especially in scenarios where real-time processing is not a requirement. ETL is synonymous with traditional data warehouses, where historical data is crucial for decision-making and analytical insights.

‍What is ELT?

More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:

• Faster processing times and loading speed
• Better scalability at a lower cost
• Support of more data sources (including Cloud apps), and of unstructured data
• Ability to have no-code data pipelines
• More flexibility and autonomy for data analysts with lower maintenance
• Better data integrity and reliability, easier identification of data inconsistencies
• Support of many more automations, including automatic schema change migration

For simplicity, we will only use Azure Blob Storage ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

How Azure Blob Storage Integration Benefits Data Warehousing?

Companies might do Azure Blob Storage ETL for several reasons:

1. Business intelligence: Azure Blob Storage data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.
2. Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operations
3. Compliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.

Overall, ETL from Azure Blob Storage allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

Choosing the Right Azure ETL Solution

As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Azure Blob Storage ETL solution.

Here is our recommendation for the criteria to consider:

• Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
• Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
• Ability to build new connectors: all data integration solutions support a limited number of data sources.
• Support of change data capture: this is especially important for your databases.
• Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
• Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
• Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
• Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
• Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
• Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
• Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

Top Azure Blob Storage ETL tools

Here are the top Azure Blob Storage ETL tools based on their popularity and the criteria listed above:

1. Airbyte

Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

What's unique about Airbyte?

Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..

Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.

Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

2. Fivetran

Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.

Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.

What's unique about Fivetran?

Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.

Here are more critical insights on the key differentiations between Airbyte and Fivetran

3. Stitch Data

Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.

Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be  maintained by the open-source community.

What's unique about Stitch?

Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.

Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.

Other potential services

Matillion

Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

What's unique about Matillion?

Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.

Here are more insights on the differentiations between Airbyte and Matillion.

Airflow

Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

What's unique about Airflow?

Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

Here are more insights on the differentiations between Airbyte vs Airflow.

Talend

Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

What’s unique with Talend?

What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.

Pentaho

Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

What is unique about Pentaho?

What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.

However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

Informatica PowerCenter

Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.

Microsoft SQL Server Integration Services (SSIS)

MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.

HevoData

HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.

All those ETL tools are not specific to Azure Blob Storage, you might also find some other specific data loader for Azure Blob Storage data. But you will most likely not want to be loading data from only Azure Blob Storage in your data stores.

Which data can you extract from Azure Blob Storage?

Azure Blob Storage's API provides access to various types of data, including:

• Unstructured data: This includes any type of data that does not have a predefined data model or structure, such as text, images, videos, and audio files.
• Structured data: This includes data that has a predefined data model or structure, such as tables, columns, and rows.
• Semi-structured data: This includes data that has some structure, but not enough to fit into a traditional relational database, such as JSON, XML, and CSV files.
• Metadata: This includes information about the data stored in Azure Blob Storage, such as file size, creation date, and last modified date.
• Access control data: This includes information about who has access to the data stored in Azure Blob Storage and what level of access they have.
• Logging data: This includes information about the activities performed on the data stored in Azure Blob Storage, such as read and write operations, and access attempts.

Overall, Azure Blob Storage's API provides access to a wide range of data types, making it a versatile and flexible storage solution for various types of applications and use cases.

How to start pulling data in minutes from Azure Blob Storage?

If you decide to test Airbyte, you can start analyzing your Azure Blob Storage data within minutes in three easy steps:

Step 1: Set up Azure Blob Storage as a source connector

• Navigate to the Airbyte website and create an account.
• Log in and access the "Sources" tab on the left-hand side of the screen.
• Locate the "Azure Blob Storage" connector and select it.
• Click on the "Create Connection" button.
• Provide a name for your connection and fill in the required fields, including your Azure Blob Storage account name and access key.
• Test the connection to ensure its functionality.
• Upon successful testing, save your connection by clicking on the "Save & Sync" button.
• Configure sync settings according to your preferences, such as sync frequency and data selection.
• Save your sync settings and initiate data syncing by clicking the "Save & Sync" button again.
  

Step 2: Set up a destination for your extracted Azure Blob Storage data

Choose from one of 50+ destinations where you want to import data from your Azure Blob Storage source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.

Step 3: Configure the Azure Blob Storage data pipeline in Airbyte

Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.

Conclusion

This article outlined the criteria that you should consider when choosing a data integration solution for Azure Blob Storage ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Azure Blob Storage ETL and how to best do it.

Azure ETL Tools FAQs

• What are Azure ETL tools, and how do they differ from traditional ETL solutions?
  Azure ETL tools are data integration solutions designed specifically for the Azure cloud platform. Unlike traditional ETL solutions, they are optimized for seamless integration with Azure services, offering native connectivity and enhanced performance within the Azure ecosystem.
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• Which Azure ETL tools offer the most comprehensive integration capabilities with Azure services like Blob Storage and SQL Database?
  Some of the top Azure ETL tools, such as Azure Data Factory and Azure Databricks, provide robust integration capabilities with Azure services like Blob Storage and SQL Database. These tools facilitate smooth data movement and transformation between various Azure data sources.
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• How can Azure ETL tools streamline data transformation processes and enhance overall efficiency in data pipelines?
  Azure ETL tools streamline data transformation processes by automating tasks such as data cleansing, enrichment, and validation. They offer intuitive interfaces and built-in functionalities for orchestrating complex ETL workflows, thereby improving efficiency and reducing time-to-insight in data pipelines.
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• What are the key features and functionalities that distinguish the top Azure ETL tools from one another?
  The top Azure ETL tools differentiate themselves through features such as advanced data connectors, real-time data processing capabilities, scalability for handling large data volumes, built-in security measures, and seamless integration with Azure analytics tools.
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• How do organizations typically leverage Azure ETL tools to extract insights from their data and drive business decisions effectively?
  Organizations use Azure ETL tools to extract actionable insights from their data and make informed business decisions. By harnessing these tools, businesses achieve faster time-to-market, improve data quality, optimize resource utilization, and gain a competitive edge in the data-driven landscape.