Quick Start
This page covers all the basics you need to know before starting to use Sprinkle
Last updated
This page covers all the basics you need to know before starting to use Sprinkle
Last updated
Sprinkle is designed for modern .
A list of all supported data warehouses and the setup instructions are given below:
Optimized for Performance: Sprinkle is built natively for cloud warehouses. Underneath, Sprinkle does optimizations like caching of data in low-cost storage, partitioning, etc., to deliver high performance at optimal warehouse cost.
In case you don't have a data warehouse or your data is fragmented in different systems, Sprinkle helps you unify all your data into your data warehouse using the Datasource connectors. These connectors ingest data into your data warehouse, and these ingestion pipelines can be setup in just a few clicks via the Sprinkle web console.
Ingest data in real-time into your data warehouse using incremental mode, where only changed or new data is ingested.
Sprinkle ingestion connectors are battle-tested, ingesting billions of rows on a daily basis in real-time.
Sprinkle automatically maps the source schema to the destination (data warehouse) schema.
JSON data is handled and flattened automatically. Any changes in the source schema are discovered and applied to the destination on the fly.
Sprinkle console shows live replication stats like the number of rows and data size moved.
A list of all supported data sources and the setup instructions are given below.
Read more about datasources here.
Before the data is used for analysis, we sometimes need to transform it to make it more analytics-friendly. Transforming your data means creating a derived table from the set of input (mostly raw data) tables.
Sprinkle provides two ways to do data transformations:
SQL Transform helps you write SQL queries using the SQL dialect supported by the warehouse. Whatever SQL you write gets executed on the warehouse directly.
Simply put, SQL Transform is the advanced SQL Editor where you can write queries, see the results, and schedule the SQL script.
The Python code gets executed on the Kubernetes cluster in the data plane. You can schedule the entire notebook to run your Python code at regular intervals.
Got 2 mins? Check out the video:
Business metrics standardize analytics across the organization, eliminating the need to write and optimize queries manually.
Unlike traditional BI tools, you can analyze data at any granularity without being a data expert. Data consumers can dive deeper into the data by building their own custom analyses and reports.
Sometimes you may quickly want to build a report for which there isn't a model defined yet. In that case, you can use SQL to build the report and create visualizations. You get a powerful SQL editor with an inbuilt Schema browser.
You can also build reports directly using tables with our intuitive report builder UI.
"Easy understanding of the product is a top-most requirement and it takes very less amount of time for a user to get familiarised with basics in sprinkle. The Product gives multiple options to users to get and analyze the data either using Reports or custom queries. Integration with external products and dashboards is super easy."
- Rajat Jain, Data Analyst, Udaan
You can combine multiple reports to create dashboards.
You can filter the data in one go easily across all the reports within a dashboard.
You can share the dashboard with others, download it as a PDF, schedule refreshes, and do drill-downs and drill-ups.
Sprinkle lets you schedule your data ingestion or report refreshes as per the desired frequency interval. You can schedule data sources, reports, etc., to run periodically.
Sprinkle is fully secure.
Learn more about security at Sprinkle here.
We do not store your data on our servers. All data is stored and processed within your private cloud network.
If you already have a data warehouse and all your required data is present in it, you can skip to the next section, .
For creating a derived table from a set of input tables, you can use .
In certain cases, you want to use python libraries for data manipulation and preparation. Sprinkle provides a feature, via which you can write Python code and do data exploration within the Notebook editor itself. The python code gets executed on the Kubernetes cluster in the Data Plane. You can schedule the entire Notebook to run your python code at regular intervals.
In certain cases, you want to use Python libraries for data manipulation and preparation. Sprinkle provides a feature, via which you can write Python code and do data exploration within the Notebook editor itself.
help analysts build business metrics and dimensions via a visual interface. Analysts can join tables, create custom expressions, and validate data all from the visual console. This reduces the manual work that would otherwise be needed from analysts for building reports. You can create models directly on warehouse tables without any data loading, unlike traditional BI tools.
Sprinkle help data consumers analyze data with drag-and-drop functionality and build visualizations.
You can also set up email notifications if a schedule fails, succeeds, or is delayed. Learn more about .
Sprinkle finds out all the dependencies across different transformations and reports automatically, and it schedules all the data refreshes in a pipeline. You can learn more about data .
Sprinkle provides fine-grained access controls. Specific data can be shared with only a specific set of users. Refer to for more details.
Sprinkle is fully secure. Learn more about .
