Properly performing analytics can spell the difference between success and failure in today’s competitive marketplace. Yet many businesses can feel so overwhelmed by the sheer quantity of data that they are unable to draw helpful insights from it. Furthermore, without a wide array of analysis tools, analytics can become routine, redundant, and ultimately ineffective.
Luckily, AWS has a variety of analytics services that businesses all over the world use to draw crucial business insights and make better decisions about the future of their company. In addition, integration with AWS’s powerful machine learning services can help businesses make accurate projections about business performance, customer activity, market forces and so much more.
The first part of performing these powerful analytics is the preparation and integration of data. Through these processes, data from a wide variety of sources can be centralized. This way it is possible to see overarching trends that may not necessarily be evident from a singular data source.
For these processes, AWS has introduced AWS Glue, a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development.
How does it work?
AWS Glue makes analytics possible through the process of data integration. This is the process of discovering and extracting data from a variety of sources, cleaning and normalizing data, and loading that data into databases, data warehouses, and data lakes.
Using visual and code-based interfaces, AWS Glue makes it possible to implement pre-built or custom workflows to extract, transform and load data. For example, a workflow can be set up (triggered by a Lambda function) to immediately take new data as it arrives and organize it into specific S3 buckets. Using the AWS Glue Data Catalog, it is then possible to see this data in a centralized place without having to move it from where it is stored. This makes it possible to keep your data organized in whatever S3 bucket, data lake, or warehouse you choose while remaining visible in a central place.
Another use case involves the use of AWS Glue DataBrew. This service enables you to explore and experiment with data directly from your data lake, data warehouses, and databases, including Amazon S3, Amazon Redshift, AWS Lake Formation, Amazon Aurora, and Amazon RDS. You can choose from over 250 prebuilt transformations in AWS Glue DataBrew to automate data preparation tasks, such as filtering anomalies, standardizing formats, and correcting invalid values. After the data is prepared, you can immediately use it for analytics and machine learning.
Benefits
Work Faster
By automating much of the work that goes into preparing and centralizing data for analytics, AWS Glue makes it easier and faster to get the results you need, when you need them.
Work At Scale
Through data crawling, identification, and reformatting services performed automatically, it’s possible to handle large amounts of data without experiencing slowdowns.
Automated Infrastructure
AWS Glue runs in a serverless environment. There is no infrastructure to manage, and AWS Glue provisions, configures and scales the resources required to run your data integration jobs, so you pay only for the resources your jobs use while running.
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