4 simple steps for
migrating to
Apache Spark with zero coding

data warehousing lyftrondata

Create connections
between data
sources and Apache Spark

real-time data big data

Prepare a source to the Apache Spark pipeline by selecting tables in bulk

data transformation data integration

Assemble a workflow and schedule it to start the Apache Spark migration process

Apache Spark

Share your data
with third-party platforms
over API Hub

Simple and Intuitive

Simple and Intuitive

Switch to Apache Spark like a boss

High-Speed Performance

High-Speed Performance

Enjoy Apache Spark's high-speed performance with codeless data environment

Prebuilt Transformation

Prebuilt Transformation

Say goodbye to tedious manual tasks with prebuilt transformation templates

Monitoring Data

Monitoring Data

Monitor your Apache Spark data frequently

apache-spark-integration

Integrate data from ERP, CRM, legacy databases, and other 300+ sources to Apache Spark

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Hear how Lyftrondata helped accelerate the data
journey of MOL Group

100X
Faster
reporting
98%
New applications
onboarded
$550K
Spend
reduction
70%
Accelerated
sales

big data Lyftrondata enables instant analytics on WNI weather data that helped us streamline shipping lines. data transformation

data integration

Koichi Tsuji

Consulting Partner at MOL Group
lyftrondata

FAQs

Apache Spark Integration is a free and open-source distributed processing system that handles big data workloads. It uses optimized query execution and in-memory caching for quick analytical queries against any size of data.

Lighting-fast processing speed: Using Apache Spark Integration software enables the users to relish the fast processing speed element.
Ease of use: Apache Spark Connectors libraries simplify the implementation of numerous major high-level operators with a Resilient Distributed Dataset.
It offers support for sophisticated analytics: Apache Spark ETL is an open-source, distributed processing system that utilizes in-memory caching and optimized query execution for faster queries.
Real-time stream processing: Users can leverage the real-time streaming processing policy with the Apache Spark Drivers tool.

No File Management System: With Apache Spark ETL there is no file management system available.

No Real-Time Data Processing: There is no real-time data processing possible with Apache Spark Connectors tool.

Expensive: The Apache Spark Integration software is quite expensive.

Small Files Issue: Users often face problems related to small files in Apache Spark Drivers tool.

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Apache Spark journey