Amazon Aurora

Website

aws.amazon.com

$ Details

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Release Date

2014 October

Categories

#Databases #Relational Databases #Tool #NoSQL Databases

Apache Hive

Website

hive.apache.org

$ Details

Release Date

-

Categories

#Databases #Big Data #Relational Databases #Data Warehousing

Amazon Aurora features and specs

• High Performance
  Amazon Aurora is designed to provide up to five times the throughput of standard MySQL and three times the throughput of standard PostgreSQL databases.
• Scalability
  Aurora scales storage automatically, growing from 10GB up to 128TB with no downtime. This automatic scaling makes it ideal for applications with fluctuating workloads.
• High Availability and Durability
  Aurora automatically replicates six copies of data across three availability zones and continuously backs up data to Amazon S3, ensuring durability.
• Security
  Aurora offers multiple layers of security including network isolation using Amazon VPC, encryption at rest using keys that you create and control through AWS Key Management Service (KMS), and encryption of data in transit using SSL.
• Fully Managed
  Aurora is fully managed by AWS, which automates time-consuming administrative tasks such as hardware provisioning, database setup, patching, and backups.
• Compatibility
  Aurora is compatible with MySQL and PostgreSQL, making it easier to migrate existing applications to Aurora with minimal changes.

Possible disadvantages of Amazon Aurora

• Cost
  Aurora can be more expensive than traditional RDS instances, particularly for workloads that do not fully utilize its high performance and scalability features.
• Complexity
  The numerous features and configurations can make Aurora complex to manage and tune, especially for those who are not familiar with AWS services.
• Vendor Lock-in
  Adopting Aurora ties you into the AWS ecosystem, which can make it difficult to migrate to other cloud providers or on-premises systems.
• Cold Start Latency
  Aurora Serverless can experience latency during cold starts, which can be problematic for applications requiring instant scalability.
• Limited to AWS Environment
  Aurora is only available within the AWS environment, which can be limiting if your infrastructure spans multiple cloud providers.

Apache Hive features and specs

• Scalability
  Apache Hive is built on top of Hadoop, allowing it to efficiently handle large datasets by distributing the load across a cluster of machines.
• SQL-like Interface
  Hive provides a familiar SQL-like querying language, HiveQL, which makes it easier for users with SQL knowledge to perform data analysis on large datasets without needing to learn a new syntax.
• Integration with Hadoop Ecosystem
  Hive integrates seamlessly with other components of the Hadoop ecosystem such as HDFS for storage and MapReduce for processing, making it a versatile tool for big data processing.
• Schema on Read
  Hive uses a schema-on-read model which allows it to work with flexible data schemas and handle unstructured or semi-structured data efficiently.
• Extensibility
  Users can extend Hive's capabilities by writing custom UDFs (User Defined Functions), UDAFs (User Defined Aggregate Functions), and SerDes (Serializers/ Deserializers).

Possible disadvantages of Apache Hive

• Latency in Query Processing
  Queries in Hive often take longer to execute compared to traditional databases, as they are converted to MapReduce jobs which can introduce significant latency.
• Limited Real-time Processing
  Hive is designed for batch processing and is not suitable for real-time analytics due to its reliance on MapReduce, which is not optimized for low-latency operations.
• Complex Configuration
  Setting up Hive and configuring it to work optimally within a Hadoop cluster can be complex and require a significant amount of effort and expertise.
• Lack of Support for Transactions
  Hive does not natively support full ACID transactions, which can be a limitation for applications that require consistent transaction management across large datasets.
• Dependency on Hadoop
  Hive's reliance on the Hadoop ecosystem means it inherits some of Hadoop's limitations, such as a steep learning curve and the need for substantial resources to manage a cluster.

Introduction to Amazon Aurora - Relational Database Built for the Cloud - AWS

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