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Step-by-Step Guide to Creating an AWS RDS Database Instance

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 Amazon Relational Database Service (AWS RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Instead of managing servers, patching OS, and handling backups manually, AWS RDS takes care of the heavy lifting so you can focus on building applications and data pipelines. In this blog, we’ll walk through how to create an AWS RDS instance , key configuration choices, and best practices you should follow in real-world projects. What is AWS RDS? AWS RDS is a managed database service that supports popular relational engines such as: Amazon Aurora (MySQL / PostgreSQL compatible) MySQL PostgreSQL MariaDB Oracle SQL Server With RDS, AWS manages: Database provisioning Automated backups Software patching High availability (Multi-AZ) Monitoring and scaling Prerequisites Before creating an RDS instance, make sure you have: An active AWS account Proper IAM permissions (RDS, EC2, VPC) A basic understanding of: ...
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Apache HIVE Top Features

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Apache Hive aids the examination of great datasets kept in Hadoop’s HDFS and harmonious file setups such as the Amazon S3 filesystem.


Apache HIVE Top Features


It delivers an SQL-like lingo named when keeping complete aid aimed at map/reduce. To accelerate requests, it delivers guides, containing bitmap guides.

By preset, Hive stores metadata in an implanted Apache Derby database, and different client/server databases like MySQL may optionally be applied.

Currently, there are 4 file setups maintained in Hive, which are TEXTFILE, SEQUENCE FILE, ORC, and RCFILE.

 Other attributes of Hive include:
  • Indexing to supply quickening, directory sort containing compacting, and Bitmap directory as of 0.10, further directory kinds are designed.
  • Different depository kinds such as simple written material, RCFile, HBase, ORC, and other ones.
  • Metadata depository in an RDBMS, notably decreasing the time to accomplish verbal examines throughout request implementation.
  • Operating on compressed information kept into the Hadoop environment, set of rules containing gzip, bzip2, snappy, etcetera.
  • Built-in exploiter described purposes (UDFs) to manipulate dates, cords, and different data-mining implements. Hive aids expanding the UDF set to cover use-cases not maintained by integrated purposes.
  • SQL-like requests (Hive QL), that are completely changed into map-reduce appointments.

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