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AWS Certified Data Analytics Specialty certification:

Emmanuel Odenyire Anyira's photo
Emmanuel Odenyire Anyira
·

3 min read

Exam preparation short notes

AWS services and features

Category:- Analytics

AWS Service:- Amazon EMR (Elastic MapReduce)

Introduction:

Amazon EMR (Elastic MapReduce) is a managed cluster platform provided by AWS that simplifies running big data frameworks, such as Apache Hadoop, Apache Spark, and Presto. It enables customers to quickly and easily provision and configure clusters, monitor cluster health and activity, and process data using big data frameworks.

Features and Characteristics:

  • Scalability: EMR clusters can be scaled up or down as required, based on workload demands. Additionally, users can add or remove nodes from a running cluster, without impacting the processing of data.

  • Flexibility: EMR supports various big data frameworks, including Hadoop, Spark, and Presto, providing customers with flexibility to use the framework that best fits their needs.

  • Integration: EMR integrates with a variety of other AWS services, such as Amazon S3, Amazon Redshift, and Amazon DynamoDB, making it easy to move and process data across multiple services.

  • Cost-effectiveness: EMR allows customers to pay only for the resources they use, without any upfront costs or long-term commitments.

  • Security: EMR provides a range of security features, including encryption of data at rest and in transit, network isolation, and integration with AWS Identity and Access Management (IAM).

How Amazon EMR Works:

  1. Define Data Sources: First, identify the data sources you want to analyze and make sure they are stored in a supported format, such as CSV, JSON, or Avro. The data can be stored in Amazon S3, HDFS (Hadoop Distributed File System), or other data stores.

  2. Create a Cluster: Next, create an Amazon EMR cluster and specify the desired big data framework, such as Hadoop or Spark. You can choose from a range of instance types and configure the number of nodes in the cluster.

  3. Configure the Cluster: Once the cluster is created, configure it by selecting the applications and tools you want to use, such as Hive, Pig, or Spark SQL. You can also specify the input and output locations for your data and set up any necessary security and networking settings.

  4. Run Jobs: With the cluster configured, you can submit jobs to process your data. Jobs can be written in a variety of programming languages, such as Java, Python, or Scala. You can monitor the progress of jobs and view logs to troubleshoot any issues.

  5. Terminate the Cluster: Once you have completed your analysis, you can terminate the cluster to avoid incurring unnecessary costs. You can also choose to create a new cluster in the future if you need to process additional data.

Scenarios:

  • Data Warehousing: EMR can be used to process large amounts of data from a data warehouse, such as Amazon Redshift, to provide deeper insights into the data.

  • Log Processing: EMR can be used to process log files generated by web servers, applications, or network devices, and extract useful information from them.

  • Machine Learning: EMR can be used to train and deploy machine learning models using frameworks such as Apache Spark MLlib or TensorFlow.

  • Real-Time Data Processing: EMR can be used to process and analyze real-time data streams, such as those generated by IoT devices or social media feeds.

References:

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