EMR Hadoop Ecosystem for Distributed Processing
Question
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 has multiple data sources built out of S3, SQL databases, MongoDB, Redis, RDS, other file systems.
AFS is looking for distributed processing framework and programming model that helps you do machine learning, stream processing, or graph analytics using Amazon EMR clusters Which EMR Hadoop ecosystem fulfills the requirements? select 1 option.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer : D.
Option A is incorrect -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 correct - Apache Spark is a distributed processing framework and programming model that helps you do machine learning, stream processing, or graph analytics using Amazon EMR clusters.
Similar to Apache Hadoop, Spark is an open-source,distributed processing system commonly used for big data workloads.
However, Spark has several notable differences from Hadoop MapReduce.
Spark has an optimized directed acyclic graph (DAG) execution engine and actively caches data in-memory, which can boost performance, especially for certain algorithms and interactive queries.
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark.htmlBased on the given scenario, Allianz Financial Services (AFS) is looking for a distributed processing framework and programming model to handle their big data analytics requirements using Amazon EMR clusters. The EMR cluster should support machine learning, stream processing, or graph analytics capabilities, and the data sources include S3, SQL databases, MongoDB, Redis, RDS, and other file systems.
Apache Spark is the best fit for AFS's requirements as it provides distributed processing capabilities for big data analytics. Apache Spark is an open-source distributed computing system that provides a programming model and framework for processing large-scale data sets.
Here are some of the reasons why Apache Spark is the right fit for AFS's requirements:
Machine Learning: Apache Spark has a machine learning library called MLlib that provides distributed algorithms for machine learning tasks. The library includes tools for classification, regression, clustering, and collaborative filtering.
Stream Processing: Apache Spark Streaming provides a programming model for processing real-time data streams. It supports various input sources, including Kafka, Flume, and Twitter, and output sources such as HDFS and databases.
Graph Analytics: Apache Spark GraphX provides a distributed graph processing framework for large-scale graph analytics. It supports various graph algorithms such as PageRank, Connected Components, and Triangle Counting.
Compatibility with Data Sources: Apache Spark has connectors to various data sources, including S3, SQL databases, MongoDB, Redis, RDS, and other file systems. This means that AFS can leverage Apache Spark to analyze data from all their data sources.
In contrast, Apache Hive is a data warehousing and SQL querying tool built on top of Hadoop. It is primarily used for batch processing and is not suitable for real-time data processing or machine learning tasks. Apache HBase is a NoSQL database that provides real-time read and write access to large-scale data. It is suitable for real-time data processing and serving, but it does not provide machine learning or graph analytics capabilities. Apache HCatalog is a metadata management tool for Hadoop that provides a unified interface to access data stored in different formats. It is not a distributed processing framework and does not provide machine learning or stream processing capabilities.
Therefore, based on the requirements specified in the scenario, Apache Spark is the best option to fulfill AFS's big data analytics requirements on Amazon EMR clusters.
