Building Query Platform with Presto and Apache Hive
Distributed SQL Query Engine Presto runs analytic queries. Infrastructure Automation implemented using Ansible and Terraform for Auto Launching, Auto Scaling and Auto Healing of its Cluster and Hive using AWS On-Demand EC2 and AWS Spot Instances.Presto has following Features
- It queries data in Hive MetaStore and optimized for latency.
- It has Push Data Processing Models like traditional DBMS implementations.
- It includes memory limitation for query Tasks and runs daily /weekly reports queries Required a Large Amount of Memory.
Apache Hive Features
- Hive runs Batch Processing against data sources of all sizes ranging from Gigabytes to Petabytes.
- Hive optimized for query throughput.
- Hive has Pull Data Processing Modelling.
Common challenge for Big Data Processing
- Build Data Processing & Query Platform and Cluster Management.
- Large DataSets on remote storage and use Presto for data discovery and Apache Hive, Tez For ETL Jobs.
- Infrastructure Automation for Cluster Management and deployment for it and Hive using AWS Spot Instances.
Solution for Infrastructure Automation
- Simplify, Speed Up and Scale Big Data Analytics workloads.
- Process Data from external storage using fast execution engines like it and Hive.
- Run large and complex queries.
- Cost effective using AWS spot instances as default and heal the cluster if cluster scale is smaller than the minimum cluster size.
- Automatic Scale Up and Down the cluster according to the CPU load.
Building Real Time Applications
It queries data including Hive, Cassandra, relational databases, separating computation from storage performing independent scaling. It combines data from multiple sources and allows analytics. Its features involve Mobile Administration, Printer State Detection, Configurable Alerts, Active Directory Integration, Native Printing Workflows, Device Agnostic, Geolocation, flexible licensing.Real-Time Applications of Presto on AWS
- Presto as a Service involving security features.
- SQL on Anything Presto Query Engine.
- Cost Based Query Optimisation.
- Autoscaling, Monitoring workload, predictable performance.
- Gaining insights through Apache Superset.
Real-Time Applications of Hive on AWS
- Statistical functions on Hadoop ecosystem.
- Structured and Semi-structured Data Processing.
- As Data Warehouse tool with Hadoop.
- Real-Time Data Ingestion with HBase.
- Usage of ETL and Data Warehousing tool.
- To provide SQL type environment and to query like SQL using HIVEQL.
- To use and deploy custom specified map and reducer scripts for the specific client requirements.
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