Sync data is all about processing data at scale. A drag & drop interface lets you configure your data pipelines and run them manually or at predefined schedules. Data can be processed in batch as well as in real time. The operations on your data can be executed using our predefined operators like join, sort, filters, custom formulas etc. You can also bring in custom scripts using our code engine operator to build custom data pipelines as well.
Synctactic supports a wide range of connectors from NoSQL to Relational databases, Flat Files to object stores and EDWs like redshift, big query and snowflake. Our webhook functionalities allow you to push data to the platform in real-time and also at high volume.
Pipes let you configure data sources, data operators and data destinations. Connect a wide variety of data sources, join them together, perform operations using our operator library and push them into any data destination. Run your pipes manually or schedule them at any frequency you require. You can also connect multiple pipes together to build complex workflows.
Build your business schemas with a simple drag and drop interface. The search bar lets you search for any column across your datasets and then lets you combine any column from any data source to build your own custom schema. No need to run multiple ETL jobs and write complex SQL queries. Just search, select and create.
Code Engine & Notebooks
Bring in your custom ETL scripts to quickly transition to the synctactic platform. Code engine supports python, SQL, java, scala and golang. You can also connect to a data source and explore it using jupyter, R or zeppelin notebooks.
Run your data processing jobs using serverless functions to perform parallel processing at scale. You can also switch to a spark execution by configuring a spark cluster to run on with a few simple steps. This way Synctactic provides the flexibility to run your workloads on any kind of infrastructure and lets you reap potential cost savings as well.