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Quick Start
A quick overview of salient Sprinkle features
This page covers all the basics you need to know before starting to use Sprinkle.

Built for Cloud Warehouses

Sprinkle works on modern Cloud Data Warehouses.
Good to know: Sprinkle does not store your data, all your data is stored and processed within your data warehouse. That is why having a Cloud Data Warehouse is a pre-requisite for Sprinkle. If you don't have a warehouse, Sprinkle can help you setup one.
List of all supported data warehouses and the setup instructions are given below.
  1. 1.
    ​AWS Redshift​
  2. 2.
    ​AWS Athena​
  3. 3.
    ​Databricks​
  4. 4.
    ​Google BigQuery​
  5. 5.
    ​Azure Synapse​
  6. 6.
    ​Snowflake​
Optimised for Performance: Sprinkle is built natively for Cloud warehouses. Underneath Sprinkle does optimisation like caching of data in low-cost storage, partitioning etc to deliver high performance at optimal warehouse cost.

Integrating your data

Incase 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. The datasource connectors ingest data into your data warehouse. The ingestion pipeline can be setup in just a few clicks via Sprinkle web console.
If you already have a data warehouse, and all your required data is present in it, you can skip to next section - Analysing your data​

#1 Realtime Ingestion Pipelines

Ingest data in realtime 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 daily basis in realtime.

#2 Automatic Schema Mapping

Sprinkle automatically maps the source schema to the destination (data warehouse) schema.
JSON data is handled and flatten automatically. Any changes in the source schema are discovered and applied to the destination on the fly.

#3 Live monitoring and control

Sprinkle console will show live replication stats like the number of rows and data size moved.
Live monitoring of data ingestion
List of all supported datasources and the setup instructions are given below.

#4 Datasources

Read more about datasources here.

Analysing your data

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Got 2 mins? Check out the video:
No-code Analytics Product Tour

#1 Build business metrics using Data Models​

Sprinkle helps Data Analysts to build business metrics and dimension via visual interface. Analysts can join tables and create custom expressions, validate data, all from the visual console. Sprinkle reduces the manual work needed to be done by Analysts for building reports. You can create models directly on warehouse tables, without any data loading unlike traditional BI tools.
Business metrics standardises the analytics across the organisation, eliminating the need to write and optimise queries manually.
Join warehouse tables visually without any coding
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Define business measures and dimensions directly on warehouse table
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#2 Drag and Drop Analysis using Segments​

Sprinkle Segments help data consumers to analyse data with drag and drop functionality and build visualisations. Unlike traditional BI tools, you can analyse 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.
Drag an Drop Interface

"Sprinkle helped us become data enablers from the data providers."

"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 segments or custom queries. Integration with external products and dashboards is super easy."
- Rajat Jain, Data Analyst, Udaan

#3 Analyse Event data to find conversions using Funnels​

Funnels allow you to examine how events are performed in a series. Funnels help you to calculate the conversion counts from one event to another to identify where they get converted most of the time, where there are drop-offs.
Create Funnels dynamically by putting events in series
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#4 SQL to build visualisation using Explores​

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 visualisation. You get a powerful SQL editor with inbuilt Schema browser.
Sprinkle Explores: SQL Report Builder

#5 Create Dashboards combining multiple reports

You can combine multiple Segment or Explore reports to create Dashboards. You can filter the data in one go easily across all the reports within a dashboard. You can download data in excel, share dashboard with others, schedule refresh and do drill-downs/drill-ups.
Dashboard

#6 Get Alerts on metric changes

​Metric Alert helps you receive email notifications if your business metric is changed in certain way. You can define thresholds and rules to determine the alert trigger condition.

Transforming your data

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.
Good to know: Sprinkle follows modern ELT approach. The data is transformed after arriving into your data warehouse. This decouples the transformation logic from data ingestion, providing you agility to change the transformation logic easily and independently. Also you have both raw and as well as derived tables in your central data warehouse, providing you the flexibility to use the data in other tools and for data science purposes.
Sprinkle provides two ways to do Data transformations:

#1 Transform data using SQL

For creating a derived table from a set of input tables, you can use Flows. Flows help you write SQL queries. You can use the SQL dialect supported by the warehouse. Whatever SQL, you write get executed on the warehouse directly.
Simply put, Flow is the advanced SQL Editor where you can write queries, see the results and schedule the SQL script.
Sprinkle Flow

#2 Transform data using Python

In certain cases, you want to use python libraries for data manipulation and preparation. Sprinkle provides a Notebook 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.

Discovering your data

You could have 100s of objects in your data ecosystem like Warehouse Tables, Reports, and Dashboards. Sprinkle helps you organise all these objects, so that you can easily find relevant data with ease.

#1 Data Quality Status

You can set any of the following status on Dashboards, Segments, Explores and Flows:
  • WIP (Work In Progress)
  • Verified
  • Deprecated
  • Has Issues
Status Drop-down
Simple text search to search tables, dashboards etc. You can apply filters based on Folders, Owners, Status

#3 Catalog to understand warehouse tables

Sprinkle creates a Catalog of all the data warehouse tables. Catalog helps you find and understand warehouse tables. You can add metadata like table and column description descriptions for other users to find and understand the relevant tables with ease. You can also generate table stats like missing values, frequency charts.

#4 Attach custom attributes to organise data

Sprinkle helps you organise all your objects like Tables and Dashboards based on certain attributes using Business Metadata. For example, if you have 3 different functions - operations, marketing, finance; and you want to tag all your entities to a specific function, then you can easily do so using Business Metadata.

Managing Schedules and Data Refreshes

#1 Scheduling datasources and reports

Sprinkle lets you schedule your data ingestion or report refreshes as per the desired frequency interval. You can schedule Datasource, Segment, Explore etc entities to run periodically.
You can also setup email notifications if a schedule fails, succeeds, or is delayed. Learn more about Schedules and Notifications here.

#2 Automatic data dependencies

Sprinkle finds out all the dependencies across different transformations and reports automatically, and schedules all the data refreshes in a pipeline. You can learn more about Data Dependencies here.

User Management

Sprinkle provides fine grained access controls. Specific data can be shared with only specific set of users. Refer User Management for more details.

Data Security

Sprinkle is fully secure. Learn more in Security at Sprinkle page.
Sprinkle does not store your data on its servers. All data is stored and processed within your private cloud network.
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On this page
Built for Cloud Warehouses
Integrating your data
#1 Realtime Ingestion Pipelines
#2 Automatic Schema Mapping
#3 Live monitoring and control
#4 Datasources
Analysing your data
#1 Build business metrics using Data Models
#2 Drag and Drop Analysis using Segments
#3 Analyse Event data to find conversions using Funnels
#4 SQL to build visualisation using Explores
#5 Create Dashboards combining multiple reports
#6 Get Alerts on metric changes
Transforming your data
#1 Transform data using SQL
#2 Transform data using Python
Discovering your data
#1 Data Quality Status
#2 Search
#3 Catalog to understand warehouse tables
#4 Attach custom attributes to organise data
Managing Schedules and Data Refreshes
#1 Scheduling datasources and reports
#2 Automatic data dependencies
User Management
Data Security