5 roles that you will need to fill on your AWS big-data team

Are you in the process of building big data teams for your business?
Here’s a list of people you need to be part of your data squad and the responsibilities that come with them.
It’s not hard to see that companies are obsessed with how to extract value from data they receive from customers and their operations.
Data is the most valuable resource an organization has, and it multiplies exponentially every second that you do business. Between 2003 and the dawn of civilization, we produced five exabytes worth of information. That amount is produced every 48 hours today.
This is how you can make informed decisions and deliver sustainable performance.
Despite the increasing number of business intelligence and big data products on the market, it is still important to get the most out of the data.
Understanding the responsibilities of each role in big data is the first step to assembling your own team.
Depending on your business size and your goals with your big data strategy you may not need all the data experts in-house.
The shortage of big data talent is a serious concern. As businesses realize the value of data professionals, the competition for talent is fiercer than ever. There is a growing demand for data scientists, data developers, and data engineers. By 2020, the number of open positions will exceed 700,000.
Let’s take a look and see how each role could fit in your organization.
Amazon Web Services: Big Data AWS hosts more data lakes and analytics than any other cloud service.
AWS offers many cloud products and services that can be used to help customers develop, secure and run big-data apps.
Users don’t need to maintain any infrastructure. They can immediately get to work analysing their data and scaling their resources as the data sets change.
The vendor continually adds new features to its stable of data management tools and analytical tools. This gives users access to the most recent big data and machine learning techniques on an easy-to-use platform.
Amazon S3 is a secure, scalable object storage system, Amazon Glacier is long-term backup and archiving, AWS Glue is data cataloging, and Amazon S3 is for short-term backup and archiving.
Users have many options when it comes to analysing data stored on AWS Cloud.
Amazon EMR is for big data processing. Amazon Redshift Spectrum and Amazon Redshift Spectrum are for querying and warehousing all types data.
There’s also Amazon Athena, Amazon Elasticsearch Service, which are analytical tools that allow users to analyze and monitor data in real time.
There are more AWS talent than anyone else. Take a look at our pre-screened AWS professionals to make the first step towards finding the best administrators, developers, or consultants in the market.
Search for candidates now. Essential roles for AWS big-data teamsData hygienistBig data isn’t for nothing.
Businesses, their customers, and their partners can accumulate a lot of data just by being there. Some data may not be useful. In many cases, the data won’t even be accurate, relevant, or complete.
Poor data is the worst thing for your well. Poor data will lead to poor results. A data hygienist can sort, sift and scrub your data so that you only spend analytical resources on data that might provide useful insights.
Even data that is relevant can throw a wrench in the works, especially when you’re rolling.

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