Great for small teams
Easy to integrate data and build Dashboards
Full Featured Trial for 14 Days

Great for mid-sized teams
Interactive Dashboards, Realtime pipelines, Juptyer Notebooks
Full Featured Trial for 14 Days

Great for Enterprise teams
Major Security features, More Warehouse options
Full Featured Trial for 14 Days

Features Essentials Professional Enterprise
All Datasources
Incremental ingestion
Automatic schema discovery
Realtime mode
SQL Transformations
Automatic pipeline assembly and scheduling
Sharing & Colloboration
Schedule and run Jupyter Notebooks as part of pipeline
Write code in python in Jupyter notebooks
Write code in pySpark in Jupyter notebooks
Incremental Transformation
Data Model
Sharing and Colloboration
Notification - Email & Slack
Dynamic Drill Downs
Rest API access
Metric Alerts
Embed Dashboards
Access Control
Data encryption
Roles & Permissions
Data Warehouse Options
AWS Redshift
AWS Athena
Google BigQuery
Azure Synapse
VPC Peering
On-Premise Deployment
Solution Architect Consultation

We are here to help

Ingestion is the process of replicating data from various datasources into the warehouse. The count is computed based on rows inserted or updated into the destination warehouse
Transformation are used to transform the data present in warehouse. These are expressed as steps of SQL queries. This process could involve cleansing, aggregating, joining or filtering the data. This step is often required for materialising the commonly used data in order to optimise the cost and query performance in the BI tool. Other than SQL queries, transformations can also be defined in Python, R or Pyspark using Jupyter Notebooks which can be set on Auto-run to run automatically at specified schedule.
Very often the transformations work on large fact tables which keep growing with time. For example events coming from Mobile App, orders etc. Running the transformation on full table every time wastes expensive warehouse resources, and is time consuming. By configuring the pipeline as Incremental, only new or updated rows are processed in each run.
Writing SQL queries for building reports requires SQL expertise. People unknowingly write non-optimal queries on large tables. Using Data model feature in Sprinkle, business metrics and dimensions can be defined by the Data Analyst team. Using this, reports can be created by point-and click interface, which can be used by non-technical users to self-serve their needs; without the need to learn SQL or any programming language. Another advantage of this is in Data governance, everybody in the organization works on the same definition of business KPIs (metrics); and at the same time having the flexibility to build custom reporting cuts on their own.
Data security is utmost importance to Sprinkle. Sprinkle provides two options for users to choose from how their data is stored: Option 1: Sprinkle hosted: Data of a customer is stored in a separate storage bucket which can be accessed only by customer specific access key. On deletion of the customer account, the corresponding data bucket is deleted permanently. Option 2: Connect your own Storage and Warehouse: Sprinkle does not store any customer data on its servers. All data is stored in customer provided cloud storage bucket.
You can provision any cloud data warehouse. (See the list of supported warehouses) and then point Sprinkle to that warehouse. For cloud warehouses charges, you directly pay to the cloud vendor. Alternatively Sprinkle can manage the warehouse on your behalf too. Refer the warehouse options in pricing page.
No problem. Sprinkle has been started by big data experts. We provide managed Hive services (at extra charges) where we install, manage and tune the Hive warehouse for optimal performance.
Sprinkle works on modern ELT architecture. Sprinkle replicates the raw data as it is into the warehouse. Transformations can be defined on existing data in warehouse.
Yes. Transformation pipelines and other features works on the data present in the warehouse; regardless how the data got ingested.
We understand that. You are not locked-into sprinkle. If you are using Sprinkle hosted data warehouse option, when you discontinue your data bucket is permanently deleted. You can request for data transfer to you before the data is permanently deleted. If you are connecting your own storage, all your data is with you.. Sprinkle does not store any of your data on its servers. To discontinue, you just disconnect sprinkle.
Data pipelines become tricky because they are time sensitive and often they have multiple steps. Some of these can be executed in parallel, others depend on its previous one. Most other workflow schedulers work on complex rules which needs to be manually defined. Maintaining these rules manually is very complex and non-optimal because of the ongoing transformation logic changes, data volumes fluctuations, warehouse capacity change etc. Sprinkle Automatic scheduling does not require these rules to be defined. Sprinkle compute the most optimal execution plan automatically, by constructing the DAG (Directed Acyclic Graph) based on various transformations, capacity and time constraints.
Yes. You can connect your warehouse to any BI tool of your choice. Sprinkle also provides some unique Reporting capabilities like KPI Dashboards, Metric Alerts etc which are free to use.
Yes. Segmentation feature works by defining the business KPIs and dimensions. Using it, business users can directly build rich reports themselves. The KPI first model not only helps in having a unified metrics across the organisation; but in self-serve analytics for non-technical users too.
Sprinkle helps in replicating data from data sources to warehouse. Check the list of supported data sources on our website. If you do not find the datasource that you are looking for, please reach out to us, we can quickly add as per your requirement.
Sprinkle provides fine grained access controls for pipelines, reports and data. Roles and Permissions can be assigned to users and groups to view or edit specific reports or pipelines.