Study the requirements and business use-cases
Data Lake Design and Architecture
Designing the Data Lake considering the following important factors
Recommendation for Optimzed Parameters for Container Size, Yarn Tuning etc.
Interactive Queries from Data Lake
Including Data Warehose based on use-cases
Deciding strategy to run AI/ML Engine on Historical Data
Collection Cluster which could be the interface between Data Source and Data Lake
Real Time Ingestion or Batch Mode Ingestion
Real Time Processing or Batch Mode Processing
We install and support both Hadoop Distribution and Cloudera Distribution. We also have expertise in Spark, Druid, Elasticsearch and Kafka Cluster implementation and tuning Configuring Parameters too.
We have vast experience migrating Data from on-prem DBs to Cloud Native DBs and vice-versa.
We have helped a lot of companies to move their Data from RDBMS to Hive or move the Data from Kafka to NoSQL/Druid/Elasticsearch with Spark Stream in an optimized way.
Helping Organizations to create Encryption zones over Data Lake to secure sensitive Data. We have implemented Kerberos for our Clients to Authenticate the Users and Services. We installed Apache Ranger for Centralized security administration.
