Data Processing & Integration

Easily connect to multiple Data Sources

Power your end to end data pipelines

Sparkflows provides the most powerful self-serve product for self-serve data processing and integration. Sparkflows provides connecting to multiple data sources, data cleaning, data quality, reporting.

Sparkflows seamlessly scales to Petabytes of data. Sparkflows provides both batch and stream processing.

Sparkflows is extendable for your environment. Add more processors for new connectors, change data capture, data quality, ETL etc. Sparkflows integrates with the modern data stack.

Sparkflows is end to end browser based. Seamlessly onboard hundreds to thousands of users onto the Platform. Enable them to work in teams to start creating the most advanced data solutions.

Create workflows with 250+ Processors, or code in your language of choice, Python, Scala, SQL.

Data Sources
Read data from various sources
Combine Datasets
Sessionize, Join, Dedup datasets
Enrich
Enrich with geo data, NLP etc.
Load data into serving stores
Load data into HBase, Cassandra, ElasticSearch etc. 
Connect all your Data

Easily connect with Data Source of your choice with built-in connectors. Sparkflows provides a wide selection of data sources to choose from to meet your needs today and in the future.

  • ​File stores : HDFS, Apache HIVE, Amazon S3

  • NoSQL stores : HBase, Cassandra

  • SQL stores : JDBC, HIVE

  • ​Streaming stores : Kafka, Amazon Kinesis, Sockets

  • Others : ElasticSearch, MongoDB etc.

 

Run streaming jobs by connecting to streaming stores​​ Sparkflows also enables you to build and use your own Connectors.

Innovate faster

                    Sparkflows enables you to quickly build out your pipelines for simple to most complex of requirements. Deploy them with one click on any of your Big Data Environment - whether you are on the cloud or on-premise.

                    You do not have to worry about version upgrades of your infrastructure or backward compatibility.

Data Quality

Sparkflows provides rich capabilities for Data Quality.

Sparkflows uses machine learning-enabled de-duplication, validation, and standardization methods to clean data for the highest quality for the multi data operations. Data is enriched in various ways.

Sparkflows has built-in processes for statistical analysis of data values to evaluate frequency, distribution and completeness of data. eg. Histograms etc.

Various inbuilt algorithms( eg. Jarowrinkler, Levenstein etc.) for data comparison so that similar but slightly different records can be matched. Dedup processors for removing duplicates.

Data validation1.png

Various in-built processors for validating email addresses , range of values, dates etc.

Data enrichment.png

Various in-built processors for data enrichment.

Innovate with your own Processors
  • Sparkflows is extendable for your environment. Add more processors based on your needs. Sparkflows integrates with the modern data stack.

  • Sparkflows Processors have schema propagation, interactive execution, scale to petabytes of data and can also provide visualizations.

Speed up Data Analytics with fast Data Preparation

Data analysts spend up to 80% of their time cleaning data instead of analyzing it. Speed up data preparation time 10-30x faster with Sparkflows.

With Interactive Execution, view the output of any processor instantly, thus quickly iterating to get your data to a clean state. With powerful data validation rules, seamlessly validate and drop invalid records.

Deploy and Run
  • Run your workflows with one click, schedule them or trigger them by event. Easily view the results of past executions.

  • Or run them with the scheduler of your choice as Sparkflows is an open system.

Enterprise Scalability

Easily scale horizontally to petabytes of data. Sparkflows also allows you to control the persistence level of DataFrames, execution parameters etc. to ensure you are not limited in any way.

Sparkflows processors are written to run at extreme scale. Save millions of dollars by running faster with efficient algorithms.

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