TL;DR Short answer: to convert JSON to a database you need a tool that flattens nested objects and arrays into columns without losing data. Thirteen options, and the deciding factor is how each handles nesting and schema drift:
Airbyte Fivetran Stitch Matillion Airflow Talend Pentaho Informatica PowerCenter Microsoft SSIS Singer Rivery Hevo Data Meltano These ETL and ELT tools help in extracting data from JSON File and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one.
How do you convert JSON to a database table? To convert JSON to a database table you have to solve one problem first: JSON is hierarchical and a table is flat. A single record can contain nested objects several levels deep and arrays of arbitrary length, none of which map cleanly onto rows and columns. How a tool handles that, by flattening nested fields into columns, unnesting arrays into separate tables, or dropping the whole document into a JSON column, is what separates the options below. The second problem is that JSON has no enforced schema, so a field that was a string in one record can be an object in the next.
For simplicity, this guide uses "JSON ETL" to refer to all data integration tools, ETL and ELT alike, that can read JSON files.
Why load JSON data into a warehouse? Business intelligence: JSON File data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.How should you choose a JSON ETL tool? Which JSON ETL tools should you consider?
Tool Type Handling nested JSON Pricing
Airbyte Open-source ELT Reads the embedded structure and normalises nested fields into columns Free self-hosted; Cloud capacity-based Fivetran Managed ELT Automatic schema handling for nested fields Monthly active rows Stitch Extract and load Flattens on load; no user-defined transformations Rows per month Matillion ELT Flattening pushed into your warehouse Credits Apache Airflow Orchestrator You write the parsing and flattening in Python Free, you run it Talend Integration platform Dedicated JSON components in the studio Quoted, no free tier Pentaho ETL and analytics JSON input step for nested structures Open core, paid enterprise Informatica PowerCenter Enterprise ETL Hierarchical parser, heavier setup Quoted; 10.5 out of standard support Microsoft SSIS ETL Needs a script component for JSON Included with SQL Server Singer Tap and target spec Spec is JSON-based; taps vary in maturity Free Rivery Cloud ELT Built-in flattening; now Boomi Data Integration Credits Hevo Data Managed ELT Auto-flattening of nested fields Events per month Meltano CLI-first ELT Singer taps managed as a Git project Free, you run it
Here are the top JSON File 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—550+ 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 750+ sources 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? Since Qlik acquired Talend, and Stitch with it, in 2023, Stitch has become one product line inside a much larger portfolio, and Qlik now publishes a formal migration path from Stitch to Qlik Talend Cloud. It is still quick to set up, but that direction of travel is worth weighing first.
Here are more insights on the differentiations between Airbyte and Stitch , and between Fivetran and Stitch.
What else should you consider? 4. Matillion Matillion is an ELT platform created in 2011, built around pushdown transformation that runs inside your cloud warehouse. It supports 100+ connectors and covers extract, load and transform. It also integrates with dbt, which has shipped with the product since version 1.70.
What's unique about Matillion? Running in your own cloud account means JSON data stays inside your infrastructure, though a multi-cloud setup may need more than one instance. Transformation is pushed down to the warehouse, so flattening large nested documents uses compute you already pay for.
Here are more insights on the differentiations between Airbyte and Matillion .
5. 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 and Airflow .
6. 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.
7. 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.
8. 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.
9. 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.
10. Singer Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
11. Rivery Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
12. 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.
13. Meltano Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
All those ETL tools are not specific to JSON File, you might also find some other specific data loader for JSON File data. But you will most likely not want to be loading data from only JSON File in your data stores.
JSON File provides access to a wide range of data types, including:
- User data: This includes information about individual users, such as their name, email address, and account preferences.
Overall, JSON File provides a comprehensive set of data that can help businesses better understand their customers, products, and performance.
How do you start pulling data from JSON files? If you decide to test Airbyte, you can start analyzing your JSON File data within minutes in three easy steps:
Step 1: Set up JSON File as a source connector 1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
Step 2: Set up a destination for your extracted JSON File data Choose the destination where you want your JSON data to land. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the JSON File 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.
Which JSON ETL tool should you choose? This article outlined the criteria that you should consider when choosing a data integration solution for JSON File 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 JSON File ETL and how to best do it.
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