12/20/2023 0 Comments Metabase cost![]() ![]() Custom SQL: This allows users to write their own SQL queries to access and manipulate data in any way they choose. Examples include calculating average revenue per customer or total sales by product category. Aggregations: These are used to summarize data by grouping it into categories and calculating metrics for each category. Examples include joining customer data with sales data to analyze customer behavior. Joins: These are used to combine data from multiple tables or sources. Examples include date ranges, customer segments, and product types. Filters: These are criteria that can be used to limit the data returned by a query. Examples include date, location, and product category. Dimensions: These are attributes that can be used to group or filter data. Examples include revenue, website traffic, and customer satisfaction scores. Metrics: These are numerical values that can be used to measure performance or track progress over time. Metabase's API provides access to a wide range of data types, including: 1. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform. This gives you great visibility and control over your data.ĭata 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. If a sync fails because of a stream, you can relaunch that stream only. 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.Īirbyte also provides stream-level control and visibility. All Airbyte connectors are open-source which makes them very easy to edit. Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. ![]() Major users include brands such as Siemens, Calendly, Angellist, and more. 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. You might want to expand to Europe, so you would need them to be GDPR-compliant too.Īirbyte is the leading open-source ELT platform, created in July 2020. So checking the level of certification (SOC2, ISO) of the tools is paramount. 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.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.The whole point of using ETL solutions is to give back time to your data team. Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need.Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt.Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc.Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?.Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature.Support of change data capture: this is especially important for your databases.Ability to build new connectors: all data integration solutions support a limited number of data sources.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?.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?.Here is our recommendation for the criteria to consider: ![]()
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