Mozart is the easiest way for teams to automate data collection and transform it into actionable insights. Mozart manages your data pipeline and runs your data warehouse. We take this tedious work off your data engineer's desk, so they can focus on what you hired them for.
Mozart connects to over 120 data sources out of the box.
See the full list.
We assign every new customer a dedicated analyst and support engineer when they sign up with us. If your team is using Slack, @mention one of these people in your company's shared slack channel and we'd be happy to help. You can also email [email protected]
Fivetran is a service Mozart Data uses to copy data into our data warehouse. Mozart uses Snowflake as its data warehouse. Snowflake is a particularly good choice of database for doing analytics.
Snowflake is both the highest-performing and the most cost-effective data warehousing solution in the marketplace today. It has become the de facto market leader by shattering the old paradigm of coupled storage and compute resource allocation, which would often times lead to costly and inflexible data infrastructure that required dedicated maintenance and optimization operations and unnecessary provisioning of additional storage or compute to satisfy the necessity of the other, while still presenting the risk of untimely data consumption bottlenecks and failures. Not only does Snowflake tailor your spend to your needs, it also helps you reduce latency and cost by applying sophisticated performance optimizations such as self-organizing storage and new compute scaling, bursting and caching capabilities, features that make accessing your data products faster and more reliable through the traditional pain points of explosive growth spurts on the input or output side of your data stack.
Read more on our blog
Mode has a good one. Coincidentally, they are a great BI Tool.
How do I write data directly to Mozart's warehouse with a tool like Retool or a custom program I wrote myself?
Before you begin, make sure that:
- You don't want to edit data that Mozart is already syncing with a connector. If this is the case, write to the data source that Mozart is syncing so that you have one source of truth.
- If your program already knows how to write to a CSV file, a database, or any other data source a connector can sync to Mozart it might be easier to write to that source and then use a connector to import that source into Mozart than to modify your program to write to Mozart directly.
I still want to write directly to Mozart's warehouse:
When you create your account Mozart creates a user with read and write access to your data warehouse. You can use this user to log in any tool that can write to a Snowflake database. Go to the integrations page for examples and login credentials.
If you decided to write data to Mozart directly, create a new table inside of a new schema. Schemas and tables that are owned by a connectors or transforms will have the table's contents rewritten every hour by Mozart's automated processes and your writes will get lost.
Updated 5 months ago