HDFS + Streaming Solution

Our Data Lake solution is built using a powerful combination of Hadoop Distributed File System (HDFS), Spark Streaming and Kafka Streams technologies. This architecture allows us to provide our customers with a robust and scalable platform for storing, processing, and analyzing large volumes of data in real-time.

At the core of our Data Lake is the Hadoop Distributed File System (HDFS), a distributed file system designed to store and manage massive amounts of data across a cluster of commodity hardware. HDFS offers fault tolerance, high throughput, and horizontal scalability, making it an ideal choice for building a scalable and reliable data storage layer. It allows our customers to store structured and unstructured data in its raw form, without the need for predefined schemas or data transformations.


Benefits Company


DevOps, Data Solution



Million USD MF Cost
Application Integration
Million Employees
0 +

How System Work

Data from various sources, including databases, applications, and IoT devices, is ingested into the Data Lake. The Hadoop Distributed File System (HDFS) serves as the storage layer, providing a distributed and fault-tolerant file system that can handle large-scale data storage. Data is stored in its raw form, eliminating the need for upfront data transformations or predefined schemas, which allows for flexibility and scalability.

Once the data is ingested into the Data Lake, Spark Streaming and Kafka Streams come into play. Spark Streaming enables real-time data processing and analytics by dividing the data into micro-batches and applying transformations, calculations, and machine learning algorithms. It provides near real-time insights and analysis, allowing organizations to make timely decisions based on the most up-to-date information.

Kafka Streams complements Spark Streaming by providing a scalable stream processing framework. It allows for the integration of real-time data streams with batch data, enabling continuous processing and analysis. Kafka Streams provides fault tolerance and scalability, ensuring that organizations can handle high-throughput data streams reliably.

Value Add

Our Data Lake system enables data archival and storage optimization. Mainframe systems often store large amounts of historical data, leading to increased storage costs. With our Data Lake solution, organizations can implement intelligent data archiving and storage practices. By leveraging HDFS and its distributed file system capabilities, organizations can store vast amounts of data in a cost-efficient manner.

The Data Lake allows organizations to optimize data storage, retaining only the necessary data for analytics and decision-making purposes, while securely storing the rest in an affordable and scalable manner. This optimized storage approach helps reduce mainframe costs by minimizing storage requirements and associated expenses.

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