Unable to find what you're searching for?
We're here to help you find itThe "Building Batch Data Analytics Solutions on AWS" course offers an in-depth exploration into constructing robust data analytics pipelines on the AWS platform. It equips learners with the skills to leverage AWS services for high-performance analytics, focusing on Batch data processing using tools like Amazon EMR and Apache Spark.
Module 0 sets the stage by introducing key Data analytics use cases and the crucial role of Data pipelines for effective analytics. Module 1 dives into Amazon EMR, detailing its use in analytics solutions, Cluster architecture, Cost management, and includes an interactive demo for launching an EMR cluster. Module 2 looks at optimizing storage and Data ingestion techniques for Amazon EMR.
Module 3 is dedicated to high-performance analytics using Apache Spark on Amazon EMR, including practical labs for hands-on experience. Module 4 continues with processing and analyzing batch data using Apache Hive and HBase on Amazon EMR.
In Module 5, learners discover Serverless data processing and orchestrate workflows with AWS services like AWS Glue and AWS Step Functions. Module 6 covers the vital aspects of security, monitoring, and troubleshooting of EMR clusters, concluding with a design activity for a batch data analytics workflow. Finally, Module 7 provides insights into developing Modern data architectures on AWS, broadening the scope for learners to design comprehensive analytics solutions. This course is a valuable resource for professionals seeking to enhance their batch data analytics capabilities on the AWS cloud.
Successfully delivered 10 sessions for over 217 professionals
Purchase This Course
♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
1-on-1 Training
Schedule personalized sessions based upon your availability.
Customized Training
Tailor your learning experience. Dive deeper in topics of greater interest to you.
Happiness Guaranteed
Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.
Destination Training
Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.
Fly-Me-A-Trainer (FMAT)
Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.
To ensure that participants are equipped to successfully undertake training in the "Building Batch Data Analytics Solutions on AWS" course, the following minimum prerequisites are recommended:
These prerequisites are meant to provide a foundation that will help learners more effectively absorb the course content and participate in hands-on labs and demos. However, individuals with a strong desire to learn and a commitment to expanding their skills may find that they can successfully complete the course even if they do not meet all of the above criteria.
This course covers advanced data analytics on AWS, focusing on batch processing and data pipeline optimization for IT professionals.
This course empowers students with the skills necessary to build scalable batch data analytics solutions on AWS, leveraging tools such as Amazon EMR, Apache Spark, and Hive.
These objectives and outcomes are designed to provide a comprehensive understanding of building and optimizing batch data analytics workflows on AWS, preparing students to create robust, secure, and cost-effective data solutions.
Amazon EMR (Elastic MapReduce) is a cloud service from AWS that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, to analyze vast amounts of data. Users can efficiently process data across dynamically scalable AWS resources, making it a cost-effective solution for aggregating, analyzing, and processing large batches of data. EMR supports various data formats and integrates with AWS storage solutions like S3, making it an excellent tool for building batch data analytics solutions on AWS. It is also often used in conjunction with AWS training data analytics to enhance data processing and analytics skills.
Apache Spark is an open-source, unified analytics engine for large-scale data processing. It efficiently handles both batch and real-time data analytics. Spark offers high-speed analytics and can process data from a variety of sources including Hadoop clusters, integrating seamlessly with big data platforms like AWS. It's designed to be fast and general-purpose, making it suitable for tasks ranging from machine learning to web analytics. With Spark, developers can write applications quickly in languages like Python, Java, and Scala, helping teams build and manage scalable data analytics solutions efficiently.
Batch data processing is a method where data is gathered, stored, and then processed in large, discrete batches at scheduled times or when enough data accumulates. This approach suits tasks that do not need immediate processing results. Businesses use this technique to handle large volumes of data efficiently, often using platforms like AWS for robust, scalable solutions. Batch processing is optimal for end-of-day reports, payroll activities, or applying updates to databases, where processing can occur without the need for real-time data analysis, reducing the cost and complexity of data handling.
Cluster architecture refers to a system design where multiple servers or computers work together as a single entity to manage workloads and process data. This setup increases reliability and scalability, as tasks are distributed across various nodes, reducing the chance of system failure and improving performance. In cluster architecture, if one node fails, others can take over its tasks without disrupting the overall system. This design is critical for high-availability applications and services, ensuring minimal downtime and consistent access to network resources and data.
Cost management in a professional context involves the process of planning, estimating, budgeting, and controlling costs to ensure a project or department operates within the approved financial framework. It encompasses all activities designed to monitor and manage spending, aiming to improve efficiency and reduce unnecessary expenses. Effective cost management strategies ensure that projects are completed on time and within budget, boosting overall profitability and financial stability for businesses or organizations. This practice is crucial across various industries, including technology, where strategic allocation of resources can significantly influence the success of projects and operational sustainability.
Data ingestion is the process of gathering and importing data for immediate use or storage in a database. Techniques include **batch processing**, where large volumes of data are collected and processed at scheduled intervals, and real-time processing, where data is continuously ingested and analyzed instantly. Selecting the right method depends on the data's nature and the urgency of the need for processed data. In contexts like **AWS training for data analytics**, understanding these techniques is crucial as they determine how effectively data can be managed and utilized for building robust analytics solutions.
Apache Hive is a data warehousing tool in the Hadoop ecosystem that facilitates querying and managing large datasets residing in distributed storage. Hive allows professionals to write SQL-like queries, called HiveQL, to analyze data, which is then internally converted to MapReduce, Tez, or Spark jobs. It is designed to handle petabytes of data, and it supports analysis of large datasets stored in Hadoop's HDFS or other compatible storage systems. Hive is crucial for businesses needing an efficient way to perform data analytics on large volumes of batch data, making it essential in scenarios demanding massive data aggregation and analysis.
Serverless data processing refers to a method where you can build and run applications that automatically manage the infrastructure. This means you don't worry about maintaining servers. It allows you to focus more on your application's logic and less on managing hardware. Tasks like batch data analytics become more efficient because you only pay for the resources you actually use and when you use them. AWS offers tailored tools for serverless data processing, enabling developers to scale without managing servers. This makes it ideal for building robust, scalable batch data analytics solutions without in-depth infrastructure knowledge.
AWS Glue is a managed extract, transform, and load (ETL) service that makes it simple to prepare and load your data for analytics. You can use AWS Glue to organize, cleanse, validate, and format large datasets before moving them into data storage systems for analysis. AWS Glue automatically discovers and categorizes your data, stores your data's schema in the Glue Data Catalog, and provides a managed, scalable environment to run your ETL jobs. This service integrates seamlessly with Amazon S3, RDS, and Redshift, allowing you to easily build batch data analytics solutions on AWS.
AWS Step Functions is a serverless orchestration service that lets you automate and coordinate multiple AWS services into flexible workflows. It's particularly useful for building complex, multi-step applications, allowing you to manage transitions between different AWS tasks efficiently. With Step Functions, you can design and run workflows that piece together services such as AWS Lambda (for computing) and Amazon S3 (for storage) to create robust, scalable applications. This simplifies the process of building applications and enables you to focus on higher-level business logic rather than infrastructure management.
Data pipelines are a series of processing steps that transfer data from one system to another, transforming it into a format useful for analysis. Imagine a factory assembly line, but for data, where raw data enters and insightful information emerges ready for decision-making. In building batch data analytics solutions on AWS, these pipelines enable efficient handling and analysis of large sets of data stored in different formats and locations. AWS training in data analytics focuses on optimizing these pipelines to maximize data usability, enable real-time decision making, and harness the full potential of cloud resources.
Modern data architectures on AWS refer to sophisticated methods of organizing and managing vast amounts of data using various AWS (Amazon Web Services) technologies. These architectures typically integrate a variety of services such as data lakes, real-time analytics, and machine learning to handle both batch and streaming data efficiently. By using these AWS services, organizations can build scalable, flexible, and cost-effective solutions for big data analytics. This setup not only supports advanced data analytics at scale but also streamlines operations and data storage, enabling effective decision-making and strategic business insights.
Data analytics involves examining large sets of data to uncover hidden patterns, correlations, and insights. In various industries, this can help in making informed decisions. For example, in healthcare, data analytics can predict disease outbreaks, improve patient care, and manage operational costs. In retail, it enables personalized marketing strategies, efficient supply chain management, and better customer service. Financial services use it for risk analysis and fraud detection. Transportation sectors improve route planning and fuel efficiency. Each of these cases involves collecting, processing, and analyzing data to enhance performance, reduce costs, and drive strategic decisions.
This course covers advanced data analytics on AWS, focusing on batch processing and data pipeline optimization for IT professionals.
This course empowers students with the skills necessary to build scalable batch data analytics solutions on AWS, leveraging tools such as Amazon EMR, Apache Spark, and Hive.
These objectives and outcomes are designed to provide a comprehensive understanding of building and optimizing batch data analytics workflows on AWS, preparing students to create robust, secure, and cost-effective data solutions.
