Turn Massive Data into Massive Insights
Every day, your business generates petabytes of data—from clickstreams and IoT sensors to logs and social media. Traditional systems can't keep up. Big Data analytics is the answer: processing and analyzing massive datasets to uncover patterns, predict trends, and drive real-time decisions.
We build and manage enterprise-scale big data platforms using the industry's most powerful technologies: Hadoop, Spark, Kafka, and modern data lakes. Whether you need batch processing, real-time streaming, or advanced analytics, we help you extract value from data at any scale.
100+
Big Data Deployments10PB+
Data Processed DailyMillions
Events/sec Streaming5x
Faster Time-to-Insight
Big Data Capabilities
Comprehensive solutions for enterprise-scale data
Data Lake Implementation
Build scalable data lakes to store raw, structured, and semi-structured data at petabyte scale.
- Data Lake Architecture
- Lakehouse Patterns
- Delta Lake / Iceberg
- Data Cataloging
Real-Time Stream Processing
Process millions of events per second with low latency for real-time analytics and action.
- Apache Kafka / Confluent
- Apache Flink
- Spark Streaming
- Kinesis / PubSub
Distributed Processing
Process massive datasets in parallel using distributed computing frameworks.
- Apache Spark
- Hadoop MapReduce
- Hive / Presto / Trino
- Ray / Dask
NoSQL Databases
Leverage NoSQL databases for high-velocity, high-variety data workloads.
- MongoDB / Cassandra
- HBase / DynamoDB
- Elasticsearch
- Neo4j (Graph)
Big Data Analytics & Querying
Run complex analytics and interactive queries on massive datasets.
- Interactive SQL (Presto/Trino)
- Data Exploration
- Log Analytics
- Business Intelligence
Data Governance & Security
Secure and govern your big data environment with enterprise-grade controls.
- Data Lineage
- Access Control (Ranger/Sentry)
- Data Masking
- Audit & Compliance
Lambda Architecture: Batch + Speed + Serving
Handling massive data with both accuracy and low latency
Batch Layer
Process all historical data with comprehensive accuracy. Results are stored in batch views.
Technologies: Spark, Hive, MapReduce
Speed Layer
Process real-time data with low latency to compensate for batch layer delays.
Technologies: Kafka, Flink, Spark Streaming
Serving Layer
Merge batch and real-time views to serve low-latency queries.
Technologies: HBase, Cassandra, Druid
We also implement Kappa Architecture for simpler streaming-only use cases. We help you choose the right architecture for your needs.
Big Data Platforms We Master
Deep expertise in open-source and cloud-native big data technologies
Apache Spark
Unified analytics engine for large-scale data processing.
Apache Kafka
Distributed event streaming platform.
Apache Hadoop
Ecosystem for distributed storage and processing (HDFS, YARN, Hive).
Databricks
Unified data analytics platform based on Spark.
AWS EMR
Managed big data platform on AWS.
Azure HDInsight
Managed big data service on Azure.
Google Dataproc
Managed Spark and Hadoop on Google Cloud.
Snowflake
Data cloud with semi-structured data support.
Big Data by Industry
Real-world applications of big data analytics
Financial Services
- Fraud detection in real-time
- Algorithmic trading
- Risk analytics
- Customer 360
Retail & E-commerce
- Real-time personalization
- Clickstream analytics
- Inventory optimization
- Customer sentiment
Healthcare
- Genomic data processing
- Patient monitoring (IoT)
- Population health analytics
- Drug discovery
Manufacturing
- Predictive maintenance
- IoT sensor analytics
- Quality control
- Supply chain optimization
Telecommunications
- Network monitoring
- Churn prediction
- Call detail record (CDR) analysis
- Fraud management
Cybersecurity
- Security log analysis
- Anomaly detection
- Threat intelligence
- SIEM integration
Our Big Data Methodology
Building scalable, future-proof data platforms
We follow a proven approach to design, build, and operate big data platforms that deliver value at scale.
Use Case Discovery
We identify high-value use cases, define success metrics, and assess data sources and volumes.
Platform Architecture
We design the data platform architecture—batch vs. streaming, storage, processing, and serving layers.
Data Pipeline Development
We build scalable data pipelines for ingestion, processing, and serving, with robust error handling.
Testing & Optimization
We test at scale, optimize performance, and tune for cost and speed.
Deployment & Governance
We deploy to production, implement data governance, and set up monitoring and alerting.
Ongoing Optimization
We continuously monitor, tune, and evolve the platform as data grows and use cases expand.
Success Stories
Real results from our big data projects
Real-Time Fraud Detection
Built a real-time fraud detection system processing 1M+ transactions/sec using Kafka, Flink, and Cassandra.
IoT Predictive Maintenance
Implemented a streaming analytics platform processing sensor data from 100k+ industrial machines for predictive maintenance.
Clickstream Analytics Platform
Built a clickstream analytics platform on Databricks processing 5B+ events/day for real-time personalization.
Tools & Technologies
Modern big data stack
Spark
Kafka
Flink
Hadoop
Hive
Presto
Cassandra
MongoDB
Elasticsearch
Databricks
Snowflake
Airflow
Ready to Harness Big Data?
Let's discuss how our big data analytics expertise can help you process massive datasets, uncover insights, and drive real-time decisions.
Frequently Asked Questions
Common questions about big data analytics
Big data refers to extremely large and complex datasets that traditional data processing tools cannot handle efficiently. It's often characterized by the 5 V's: Volume (scale), Velocity (speed), Variety (different formats), Veracity (data quality), and Value (business value).
Traditional data warehouses store structured, processed data optimized for BI and reporting. Big data platforms can store raw, semi-structured, and unstructured data at massive scale, and support advanced processing like real-time streaming and machine learning. Many modern architectures combine both.
It depends on your use case. Hadoop MapReduce is batch-oriented and can be slower. Spark is faster for many workloads (in-memory processing) and supports streaming, SQL, and ML. Many organizations use both—Hadoop for storage (HDFS) and Spark for processing.
We use stream processing technologies like Apache Kafka for ingestion and Apache Flink/Spark Streaming for real-time processing. This enables use cases like fraud detection, real-time personalization, and IoT monitoring with sub-second latency.
A data lake is a centralized repository that stores all your data—structured and unstructured—at any scale. You can store data as-is, without having to structure it first. Data lakes are often built on cloud storage (S3, ADLS, GCS) with processing engines like Spark.
We implement data quality frameworks with validation rules, anomaly detection, and data profiling. We also use tools like Apache Griffin and Great Expectations, and establish data governance practices to maintain quality over time.