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We Provide Large-scale Data Processing with Apache Spark’s Unified Analytics Engine

We achieve high performance for streaming and batch data using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.

Apache Spark
Data Streams

We can build Apache Spark applications in Java, Python, or Scala that analyze multiple data streams at the same time, such as financial transactions. These data streams can then be analyzed across the cluster to detect fraudulent behavior.

Machine Learning Algorithm Training

Our Apache Spark apps can train software to find more efficient algorithms over large sets of data. Since Spark stores data in memory rather than the hard drive, our developers create applications that can take solutions from known data sets and apply it to unknown data sets rapidly.

Data Integration
with Apache Spark

Through the use of Apache Spark, we can reduce the time and cost needed to extract data from different systems, transform it by a cleaning and standardization process, and then load it into another system to analyze the data and report on it.

Interactive Analytics with Apache Spark

Whether a business is in stocks, sales, or production, dynamic data exploration is a must: whether via SQL or another method, a system must respond and adapt to repeated similar queries. Our team can use Spark for this ideal purpose because of its ability to respond swiftly.

Apache Spark Data Services

We provide large-scale data processing using Apache Spark’s unified analytics engine.

Developing on Apache Spark

Our Apache Spark developers make sure that we deliver the right solution for you in the related domain. Our developers have years of experience building ETL pipelines, real-time processing, cluster optimization, and machine learning application development.

Predictive Analytics

We use Apache Spark to reduce the time and cost needed to extract data from different systems, build reports, and also aggregate from a large number of both static and rapidly flowing data into your existing system.

ETL Tools

Chetu’s solutions transform vast amounts of unstructured data into visualized, customizable reports enhanced with interactive dashboards. Our professionals build ETL (extract, transform, load) pipeline based on Hadoop HDFS (Hadoop Distributed File System, GCP Cloud Storage, Azure Blob storage, Amazon S3, and the DSE distributed file system.

Data Streaming

Our solutions can funnel large quantities of data by utilizing a combination of stream and real-time batch processing. We employ Apache Spark’s computation engine to undertake the calculations needed to facilitate the data stream.

DATA Warehousing

Our developers drive business intelligence by gathering, integrating, structuring, and storing data from disparate sources. We provide a consolidated view of current and historical data in one single shared repository.

Real-time Data Processing

We utilize Apache Spark to migrate and store your large data volumes using in-memory computing and other optimization techniques to realize and obtain high availability and scalability.

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Case Study

JUMP Data-Driven Video provides a business toolkit for video service providers to increase retention, customer engagement, content personalization, and marketing performance to ramp up businesses’ ROI. JUMP’s platform accumulates video service providers’ backend and frontend data sources that are enriched through big data, artificial intelligence, and machine learning capabilities.

Data Sources

We run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, and on Kubernetes.

Apache Standalone

Apache Standalone

We utilize the building/standalone cluster manager incorporated with Spark, making it easier to manage clusters. We run Apache Spark Standalone on Linux, MacOSX and Windows.

 Apache Mesos

Apache Mesos

Chetu provides sharing and resource isolation by using Apache Mesos’ general cluster manager. We also use Mesos to run Hadoop applications.

Hadoop YARN

Hadoop YARN

Our developers use YARN to expand Hadoop by allowing it to process and run data for batch processing, interactive processing and stream processing.



Our developers utilize Kubernetes for the purpose of automating deployment, scaling, and management of containerized applications.

Apache HBase

Apache HBase

Our team uses Hbase to runs on top of Alluxio and HDFS and provide bigtable capabilities for Hadoop.

Apache Cassandra

Apache Cassandra

We utilize Apache to manage large amounts of data across multiple commodity servers, providing our clients with high availability with no one point of failure.


Drop us a line or give us a ring with inquiries on Spark Streaming, Spark Core, Spark Apache, Spark SQL, Development for Apache Spark and Data Analytics Solutions. We love to hear from you and are happy to answer any questions.

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