By Tony Howlett

2018-01-17

AWS

Over the last few years, companies have patiently waited for the era of data science to become fully realized.

Microprocessors, cloud storage, and the mass adoption of the Internet gave everyone the ability to collect virtually unlimited raw data — several years before most people had the capability to easily organize or use it. But they collected it anyway, knowing that someday data mining technology would catch up, and they’d be able to turn raw data into unprecedented insights.

Today, data lakes have become the next evolution of data management, giving many companies the chance to finally wade into the data they’ve collected in preceding years.

What are data lakes?

Data lakes are unstructured data pools that can be searched using cloud-based processing power, even if companies have not taken the time to come up with defined schemas or indexes.

In the past, your ability to search data was limited in large part by processing power, which is why you organized data into databases — it made items easier and faster to find. Now with the virtually unlimited processing power of the cloud, you can throw as much power as you need to at any individual search. Concurrent advances in machine learning have further enabled the ability to search for things that you may have never explicitly labeled.

Wading into data lakes using AWS

AWS has played an important role in making data lake technology more attainable for the average company. Amazon Simple Storage Service (S3) is object storage with a web interface for fast, easy, SQL-type queries on unstructured data. Glacier Select offers similar functionality for long-term cold storage.

With the introduction of the AWS Athena, which allows SQL Queries into S3 repositories, it’s possible to search for objects and even values within objects inside S3 buckets.

These technologies are ideal for large bodies of unstructured data that might be needed for multiple analytical purposes. Examples would be telemetry data from SCADA devices, edge computing devices (such as smart cameras that could tag video footage as it comes in), and many data warehousing applications that defy typical database structures.

Getting help with your data strategy

In the near future, your success or failure will hinge on your ability to collect data, glean insights from it, optimize your processes and operations, and provide personalized experiences for customers. If you haven’t begun working on your data science strategy, AWS and Codero are offering you a great opportunity to do so.

As an AWS-certified partner, Codero can architect, implement, and manage your AWS storage solutions and ensure your team is up to speed on best practices for querying and getting value from your data.

It’s time to take the next step in your data science journey from data collection to value extraction. It’s the moment you — and your stores of big data — have been waiting for.
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