Big Data The ultimate guide AWS

Here’s everything you need to know about Big Data

The term Big Data is not something to be scared of. You hear this term and might think that it refers to something fancy and technical, that it is esoteric to the IT realm. No.

BIG DATA is just data, the difference being unprecedented volume, velocity and variety.

Volume: Terabytes to petabytes.

Velocity: Collection, storage, processing, and analysis within increasingly short intervals of time, daily to real-time.

Variety: Wide range of formats and sources like social media interactions, weblogs, financial transactions, eCommerce, and online transactions.

Here is a generic definition of big data:

Big data refers to large volumes of structured and unstructured data of an organization. Big data is analyzed for insights that result in better strategic business moves and business decisions.

Why is Big Data important?

The importance of big data is that it lends itself to insightful analysis that results in cost reductions, time reductions, new product development, and optimized offerings, as well as smart decision making. Combining big data with proper analytics can facilitate business tasks such as:

  1. Pinpointing root causes of issues, defects, and failures.
  2. Recalculating risk portfolios quickly.
  3. Detecting fraud behavior before any damage is caused.

Big data technologies are almost necessary to organizations because traditional databases are no longer able to support the volumes, variety, and velocity of data.

If big data is not efficiently managed, organizations could face burgeoning costs and plummeting productivity. It is time for such organizations to migrate heavy current workloads onto big data technologies.

Addressing Big Data challenges  

Big data technologies allow businesses to feasibly collect and store enormous datasets, as well as analyze those datasets to unravel valuable insights.

Collection of raw data: Logs, transactions, mobile devices among other things . A big data platform allows developers to absorb a wide variety of structured to unstructured data at any speed.

Storage: A scalable, secure, and durable repository for data.

Processing and analysis: Sorting, filtering, aggregating, and performing more advanced algorithms and functions. The resulting consumable data sets are stored for further processing and/or made available for consumption through data visualization and business intelligence tools.

Gain insights:  End-users may consume or view the resulting data as statistical predictions or recommended solutions.

Analytic Styles

Descriptive analytics answers the question: What happened and why?

Predictive analytics estimates the probability of a given event in the future.

Prescriptive analytics provide prescriptive actionable recommendations.

Here is an informative video by Amazon Web Services about Big Data…

Big Data on AWS

Investing in educating yourself about big data on AWS will introduce you to cloud-based big data solutions along the likes of Amazon Redshift,  Amazon EMR, and Amazon Kinesis. You learn how to use Amazon EMR for processing data using Hadoop tools such as Hue and Hive. If you want to be able to:

  • Design big data environments for cost-effectiveness and security. 
  • Work with Amazon DynamoDB, Amazon Quicksight, Amazon Redshift, Amazon Kinesis, Amazon Athena

You gotta get your hands on a great Big data course!

This course is for you!

P2L has an amazing course for you that can help you transform your knowledge about big data on AWS. 

Big Data on AWS 

Here are the prerequisite courses you may need to take advantage of this training:

Architect – Architecting on AWS

Developer – Developing on AWS

Operations – Systems Operations on AWS

Here’s what you can learn

In less than a week, only 3 days, you can learn to:

  • Integrate AWS solutions into a big data ecosystem.
  • Leverage and use Apache Hadoop in the context of Amazon EMR
  • Identify Amazon EMR cluster components as well as launch and configure an Amazon EMR cluster.
  • Leverage programming frameworks for Amazon EMR including Pig, Hive, and Streaming.
  • Leverage Hue for improving the ease-of-use of Amazon EMR
  • Use in-memory analytics with Spark on Amazon EMR
  • Choose the right AWS data storage options

Who Can Benefit from this course

This course is designed to benefit:

  • Individuals who design and implement big data solutions.
  • Solutions Architects, Data  Analysts, and Data Scientists who want to learn about the services and architecture patterns of big data solutions on AWS.

Contact P2L today to begin your Big data on AWS journey and get your hands on this course.

Learn more about Big Data on AWS

 

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