Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stock broking businesses as well as unit trust and asset administration, having served the financial community over the past five decades. AFS launched EMR cluster to support their big data analytics requirements.
AFS is looking at a data warehouse and analytics environments that provide SQL on Hadoop capabilities. Which EMR Hadoop ecosystem fulfills the requirements? select 1 option.
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A. B. C. D.Answer : A.
Option A is correct -Hive is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster.
Hive scripts use an SQL-like language called Hive QL (query language) that abstracts programming models and supports typical data warehouse interactions.
Hive enables you to avoid the complexities of writing Tez jobs based on directed acyclic graphs (DAGs) or MapReduce programs in a lower level computer language, such as Java.
Hive extends the SQL paradigm by including serialization formats.
You can also customize query processing by creating table schema that matches your data, without touching the data itself.
In contrast to SQL (which only supports primitive value types such as dates, numbers, and strings), values in Hive tables are structured elements, such as JSON objects, any user-defined data type, or any function written in Java.
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive.htmlOption B is incorrect -HBase is an open source, non-relational, distributed database developed as part of the Apache Software Foundation's Hadoop project.
HBase runs on top of Hadoop Distributed File System (HDFS) to provide non- relational database capabilities for the Hadoop ecosystem.
HBase works seamlessly with Hadoop, sharing its file system and serving as a direct input and output to the MapReduce framework and execution engine.
HBase also integrates with Apache Hive, enabling SQL-like queries over HBase tables, joins with Hive-based tables, and support for Java Database Connectivity (JDBC)
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hbase.htmlOption C is incorrect -HCatalog is a tool that allows you to access Hive metastore tables within Pig, Spark SQL, and/or custom MapReduce applications.
HCatalog has a REST interface and command line client that allows you to create tables or do other operations.
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hcatalog.htmlOption D is incorrect -Apache Phoenix is used for OLTP and operational analytics, allowing you to use standard SQL queries and JDBC APIs to work with an Apache HBase backing store.
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-phoenix.htmlThe EMR (Elastic MapReduce) service of AWS provides a managed Hadoop framework that allows users to process and analyze large datasets using a variety of Hadoop ecosystem components. In this scenario, Allianz Financial Services (AFS) has launched an EMR cluster to support their big data analytics requirements and is now looking for a data warehouse and analytics environment that provides SQL on Hadoop capabilities.
The Hadoop ecosystem includes various tools and frameworks that can provide SQL-like interfaces for data processing and analysis. Some of the popular options include Apache Hive, Apache HBase, Apache HCatalog, and Apache Phoenix.
Apache Hive is a data warehousing and SQL-like query language tool that enables users to query data stored in Hadoop Distributed File System (HDFS). Hive supports various data formats, including CSV, TSV, JSON, and ORC, and provides an SQL-like interface to query and analyze large datasets.
Apache HBase is a NoSQL database that runs on top of Hadoop and provides real-time read/write access to large datasets. It supports structured and unstructured data, and users can interact with HBase using a Java API or SQL-like query language.
Apache HCatalog provides a metadata management system for Hadoop, allowing users to access and manage data stored in various Hadoop components, including Hive, HBase, and Pig. HCatalog provides a unified schema and metadata management system that enables users to discover, access, and process data stored in different Hadoop components.
Apache Phoenix is a SQL-like query engine for HBase that provides a low-latency, high-performance interface for querying and analyzing large datasets. Phoenix supports various SQL-like features, including joins, aggregations, and window functions, and provides JDBC and ODBC drivers for integration with other tools and applications.
Based on the requirements mentioned in the scenario, AFS is looking for a data warehouse and analytics environment that provides SQL on Hadoop capabilities. Among the options given, Apache Hive and Apache Phoenix are SQL-like query engines that provide an SQL-like interface for processing and analyzing large datasets. While Apache HBase and Apache HCatalog provide access to data stored in Hadoop, they are not specifically designed to provide SQL-like interfaces for data analysis.
Therefore, the answer to this question is A. Apache Hive, as it is a data warehousing and SQL-like query language tool that enables users to query data stored in Hadoop Distributed File System (HDFS).