Big Data Analytics
Build enterprise big data platforms on Hadoop, Apache Spark, and modern data lake architectures to process and analyze massive volumes of structured and unstructured data.
CSL's Big Data practice helps organizations build modern data platforms capable of ingesting, storing, and analyzing petabytes of structured, semi-structured, and unstructured data. We deploy Hadoop-based data lake architectures on commodity server clusters, implement Apache Spark for real-time stream processing and batch analytics, and integrate with cloud data platforms including AWS EMR and Azure HDInsight. Our data engineers have delivered big data solutions for telecom operators analyzing CDRs, banks processing transaction logs, and government agencies managing census data.
Part of
Next Gen SolutionsKey Features
Hadoop Data Lake
HDFS-based data lake architecture enabling petabyte-scale storage and MapReduce/Spark processing on commodity hardware.
Real-Time Stream Processing
Apache Kafka and Spark Streaming for real-time ingestion and processing of high-velocity event data.
Data Lakehouse Architecture
Modern lakehouse combining data lake flexibility with data warehouse reliability using Delta Lake or Apache Iceberg.
ML Model Deployment
MLflow-based model management and Spark ML for building and deploying machine learning models on big data.
Use Cases
- Telecom CDR Analytics
- Financial Transaction Monitoring
- Government Census Processing
- Fraud Detection
Interested in Big Data Analytics?
Talk to our pre-sales team for a tailored recommendation, architecture review, and pricing.