Cloud infrastructure providers that support PaaS (Platform as a Service) are the ideal solution for processing and analyzing your big data faster, leading to insights that enable and foster innovation while helping your organization gain a competitive advantage. AWS (Amazon Web Services) offers a broad platform of managed services that help build, secure, and seamlessly scale end-to-end big data apps easily and quickly.
AWS Lake Formation
When AWS launched Lake Formation in late 2018 its purpose was to automate many of the tasks involved in building a data lake, which is a data repository used to store both structured and unstructured data. Up until then, a company that wanted to build a cloud architecture had to:
- Prepare its storage and configure its S3 buckets.
- Collect and move existing data from various places, adding metadata tabs in order to put it in a catalog.
- Determine how the data would be stored, including indexing and partitioning that made analysis convenient.
- Set up proper security policies including encryption and access controls.
Unfortunately, accomplishing all of this could take months, which made setting up data lakes a proposition that often stayed on the back burner. The Lake Formation service simplifies and speeds the process using just a couple clicks within the dashboard:
- Point the service at your existing S3 buckets (including data stored in NoSQL or AWS relational databases)
- Select which security and data access policies you want to apply to the data as it loads into the lake.
As the data moves into the AWS data lake, the service extracts each dataset's metadata that’s used to create a data catalog. Automated partitioning rules are applied for more efficient storage, with the option to transform data for faster analysis in downstream services like Apache Spark Streaming or AWS Kinesis. Machine learning is used to de-duplicate data, encryption can be applied to protect both inflight and stored data, and AWS Key Management Service can be used to store the private keys.
What difference does all this make to big data and cloud computing? We’re glad you asked.
The AWS Data Lakes/Analytics Connection
Today’s deluge of data has seen terabytes give way to exabytes and relational (structured) data be overtaken by unstructured. Traditional on-premises infrastructure are woefully insufficient to accommodate the volume, velocity, and variety of data being collected.
Companies are now searching for ways to achieve operational agility by seamlessly managing large volumes of data and making it usable in appropriate formats, be it real-time alerts, reports, or analytics. But where you once had to draw relevant information from disparate and expansive data assets, data lakes allow you to take a variety of analytics approaches in ways traditional data silos and warehouses cannot, and all without compromising on governance or security.
The AWS Advantage
Big data workloads are ideally suited to the cloud computing model. The AWS platform removes the vast operational complexities of managing physical infrastructure so you can easily scale applications up and down based on demand. Which AWS tool you use for big data analytics depends on the characteristics and requirements of your analytical workload.
Some questions to ask:
- Do you need analytic results in real-time, in seconds, or over a longer timeframe?
- Will the analytics provide significant value?
- What budget constraints exist?
- How large is the data and what is its growth rate?
- How is the data structured?
- Vendor-neutral or vendor products?
Making things more complicated is that often there are many different, and sometimes conflicting needs on the same data set. And as more data is generated and collected, scalable, flexible, and high-performing tools are needed to provide timely insights. Keeping pace with emerging tools and choosing the right ones for your applications is a constant challenge.
The AWS platform’s broad set of managed services is designed to collect, process, and analyze big data more efficiently.
- On-premise and real-time data movement with options like AWS Snowball and Kinesis Data Streams.
- Easy, secure data storage in any format and at massive scale with Glacier and S3.
- Broad, cost-effective analytic services built for a wide range of use cases such as big data processing using Apache Spark and Hadoop, operational analytics, and interactive analysis.
- Machine learning services for predictive analytics including frameworks and interfaces, platform services, and app services with pre-built AI functionality.
The flexibility and choice AWS services bring to the table means you get to avoid updating and managing tools and spend your time focused on core business goals.
Take to the Cloud
AWS offers an array of big data analytics solutions that are designed to be cost-optimal and resilient, resulting in a flexible, big data architectures that can scale along with your business. The bottom line? Big data doesn’t necessarily mean big chaos. AWS’ automated solutions can help you unify diverse data streams, providing the timely insights your organization needs to grow and thrive.