Big Data Transactional Processing

Big Data Transactional Processing

• Cost-effective data storage strategies that meet ingestion, processing, and querying requirements of TB/PB scale datasets.
• Real-time querying of these large datasets for highly interactive use-cases.
• Deployment Architecture including sharding and replication strategies based on cluster size and hardware configuration.
• Caching solutions in application architecture desing to provide ability to deliver large amounts of data for interactive use cases without compromising performance.
• Real-time processing of huge datasets that benefit from massive parallelization using Spark and Hadoop processing frameworks.
• Memory & Performance optimizations of existing algorithms and business logic to handle large data sets.
• Real-Time analytics processing of streaming data for IoT use cases using Spark/Spark Streaming

Technology Expertise

• Storage
  • AWS DynamoDB
  • Crate.io & ElasticSearch
  • Cassandra
  • MemSql
  • Riak TS
  • AWS S3
  • AWS ElastiCache
  • MongoDB
  • HBase
• Processing
  • Hadoop MapReduce
  • Spark/Spark Streaming
  • AWS Kinesis
  • AWS Lambda