Big Data Processing & Analytics
Harness the power of big data with scalable processing frameworks and distributed computing solutions. From batch processing to real-time streaming, we help organizations handle massive datasets efficiently.
Big Data Services
Distributed Computing
Apache Spark, Hadoop, and Kubernetes-based distributed processing.
Stream Processing
Real-time data processing with Apache Kafka and Apache Flink.
Data Lake Architecture
Scalable data lakes on AWS, Azure, and Google Cloud platforms.
ETL/ELT Pipelines
Automated data ingestion and transformation workflows.
Machine Learning at Scale
Distributed ML training and inference on big data platforms.
Data Orchestration
Workflow management with Apache Airflow and Kubernetes.
Big Data Technologies
Apache Spark Ecosystem
Spark SQL, MLlib, Structured Streaming for unified big data processing.
Hadoop Ecosystem
HDFS, MapReduce, Hive, and HBase for traditional big data workloads.
Cloud Big Data
AWS EMR, Azure HDInsight, Google Dataflow for managed big data services.
Stream Processing
Apache Kafka, Apache Flink, Apache Storm for real-time data processing.
Scalable Data Lake Architecture
Modern data lake solutions that handle structured and unstructured data at petabyte scale
- Multi-format data ingestion
- Schema-on-read processing
- Cost-effective storage tiers
- Advanced query capabilities
- Integration with analytics tools
Real-time Stream Processing
High-throughput stream processing for immediate insights and automated responses
- Sub-second latency processing
- Fault-tolerant architectures
- Scalable event handling
- Complex event processing
- Integration with ML models
Processing Frameworks
Batch Processing
- Apache Spark: Unified analytics engine for large-scale data processing
- Hadoop MapReduce: Distributed processing for fault-tolerant batch jobs
- Apache Flink: Stream processing with batch capabilities
- Distributed SQL: Presto, Trino for interactive analytics on big data
Stream Processing
- Apache Kafka: High-throughput distributed streaming platform
- Apache Pulsar: Cloud-native messaging and streaming
- Apache Storm: Real-time computation system
- AWS Kinesis: Managed streaming data services
Hybrid Processing (Lambda Architecture)
Data Sources → Stream Layer (Real-time) → Serving Layer → Applications
↘ Batch Layer (Historical) ↗
Data Storage Patterns
Data Lake Architecture
- Bronze Layer: Raw data ingestion and storage
- Silver Layer: Cleaned and validated data
- Gold Layer: Business-ready aggregated data
- Data Catalog: Metadata management and discovery
Storage Technologies
- HDFS: Hadoop Distributed File System
- Amazon S3: Object storage for data lakes
- Azure Data Lake: Scalable analytics storage
- Google Cloud Storage: Multi-class cloud storage
Performance Optimization
Spark Optimization Techniques
# Example Spark optimization
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, broadcast
spark = SparkSession.builder.appName("OptimizedProcessing").getOrCreate()
# Broadcast join for small tables
large_df = spark.table("large_dataset")
small_df = spark.table("lookup_table")
result = large_df.join(
broadcast(small_df),
large_df.key == small_df.key
)
# Partitioning and bucketing
result.write.partitionBy("date").bucketBy(10, "customer_id").saveAsTable("optimized_table")
Resource Management
- Dynamic Allocation: Automatic executor scaling
- Memory Management: Optimal heap and off-heap settings
- CPU Optimization: Core allocation and threading
- Network Optimization: Shuffle and serialization tuning
Data Pipeline Architecture
ETL/ELT Workflows
- Data Ingestion: Multi-source data collection
- Data Validation: Quality checks and schema validation
- Data Transformation: Business logic application
- Data Loading: Target system population
Orchestration Patterns
# Apache Airflow DAG example
dag_config:
dag_id: big_data_pipeline
schedule_interval: '@daily'
tasks:
- extract_data:
operator: SparkSubmitOperator
application: extract_job.py
- transform_data:
operator: SparkSubmitOperator
application: transform_job.py
depends_on: extract_data
- load_data:
operator: SparkSubmitOperator
application: load_job.py
depends_on: transform_data
Monitoring & Observability
Performance Monitoring
- Spark UI: Job execution monitoring
- Ganglia/Prometheus: Cluster resource monitoring
- Custom Metrics: Business KPI tracking
- Log Aggregation: Centralized logging with ELK stack
Data Quality Monitoring
- Great Expectations: Data validation framework
- Apache Griffin: Data quality solution
- Custom Validators: Business rule validation
- Anomaly Detection: Statistical outlier identification