Analytics on Amazon are inextricably linked to AWS Big Data. With the huge amount of information traveling through digital products every single second, you need a reliable set of tools to properly implement analytics on AWS. Luckily, we know how to do that and will tell you in this article.
Table of contents
- Analytics on AWS – gathering and processing
- Analytics on AWS – visualization and analysis
- Your role as the client in the AWS Big Data analytics process
- … and justice for all
With data, you can make business decisions and steer your business in a new direction. You can also slightly adjust the course, making changes invisible to users but impacting them in meaningful ways. To utilize the power of analytics on AWS, you will need to collect the data, store it, process it, make an analysis and visualize the results.
That’s why we always recommend gathering data from the beginning. This is the easiest and most important step. The real challenge comes with processing data and creating valuable business insights.
Analytics on AWS – gathering and processing
For data collection, we use Amazon Pinpoint. We use this service in projects that require analysis similar to Google Analytics. We have sent events from the frontend (like tapping a button or sorting out a table by date) and set up Pinpoint with Amplify library. The service lets us gather, display and sort events using multiple filters and present them to you in a useful fashion.
We also connect to a service called Amazon Kinesis Data Firehose. With it, we can direct the data stream, data stores and analytics services, making them available and useful across the board, within the entire application. The very first service that benefits from this funnel is Amazon Simple Storage Service (S3).
By analogy, if something happens on the backend (like a new user registration or login process), the traffic goes through Amazon Cognito. These events can be intercepted by AWS Lambda. Later on, we can publish them in other microservices, using services like Amazon EventBridge, for example. This will help other microservices to get data and communicate its status between them. What happened, who triggered the action, what information changed, and what other microservices should now do. This is the very core of analytics on Amazon. Microservices should alway be independent. Other microservices publish information or events about what happened, but any given microservice always decides what it should do in a given situation.
With both: frontend and backend covered by AWS-related microservices; we can safely control the data flow for many projects coming our way. We know what user triggers what action in the system, what events are triggered because of the action, and what the potential threat level is if the user is unauthorized. The information can be stored (via cookies, for example) and recognized on different browsers and different devices.
When it comes to exporting data, we do it through the Amazon S3 service. We can download the information from the console through Amazon Command Line Interface (CLI) or we can create a specialized application programming interface (API) for it.
Analytics on AWS – visualization and analysis
The simplest way to visualize data is to use the Amazon QuickSight microservice. This data should be taken from somewhere, so we use Amazon Athena to draw from the AWS Big Data lake stored in S3. Athena can “ask” files (millions of records stored in JSON) about specifically needed information or an event. Athena does that with SQL, but not by copying data. The service creates a metatable with all necessary records. QuickSight asks various sources about the data and creates visualizations.
This is only one possible solution for that type of request. There are numerous services for transforming data and delivering results and handy visualizations. However, Athena can “refuse” cooperation, if data is delivered in the wrong format or without proper formatting. To overcome this challenge, we use other types of tools for an ETL (extract, transform, load).
For “special operations” we use Amazon Redshift. It’s a warehouse that’s free of limitations that traditionally understood databases struggle with. Typically, they are not useful for heavy lifting in data analytics; on AWS or otherwise. With a million records and a simple task like a summary of all banking transactions, we can safely say you’re covered. But if the amount is higher (and it usually is), you need a whole day to process and analyze that.
That’s where a warehouse comes in. It’s a distributed database. A node, if you will take all these transactions and give them (one portion at a time) to other nodes that can process and analyze a smaller chunk. This significantly improves business operations by dramatically reducing the time needed to produce results.
Another useful tool is called Amazon Elastic Map Reduce (EMR). It can also tackle the load by dividing it on a massive scale and analyzing data step by step.
We also use Amazon CloudWatch for monitoring S3 requests and more. It’s a powerful tool for DevOps, engineers, developers, and managers. We can sneak peek into the system-wide performance changes and optimize resource utilization for the optimal outcome and performance.
Finally, we use Amazon X-Ray for analytics and debugging applications. We can monitor the data flow between various microservices and respond accordingly when the load is too much or something goes wrong and there’s a need for troubleshooting. We can identify bottlenecks, which is useful, especially for AWS Big Data analytics, where there’s a lot of information. Some of it can plug the system; this is the remedy.
Your role as the client in the AWS Big Data analytics process
If you want to give us your business, we have a little homework for you. We would like to know what your endgame is? What is the business justification for gathering data? What kind of data do you want to have, process, and display? We think about Amazon QuickSight here, since it’s the primary and natural first-choice option. What metrics are important to you? A number of daily registering users, fee percentage per user (in a subscription model of your product)?
There can also be a situation when you have a nearly completed frontend. Maybe you already have implemented button placement across the application but you wonder about the effectiveness of the design. We can gather data for you and if the application is underperforming, suggest changes in the UX / UI design.
… and justice for all
The fundamental problem with data comes not with the volume, although it can be a challenge on its own. The real issue lies in the processing department. How can you effectively dig through a load of information and adjust your digital product accordingly to the market’s expectations? Cloud computing software development can help with that, but it all starts with you.
To do your product and your data justice, we need to know your end game and intentions behind gathering information. Analytics on Amazon can be supported with Amazon Web Services, Big Data support on our part, and lots of talks on a daily scrum in between. But what we really want to know is how we can help. Aside from the already mentioned, there are lots of tools to choose from (See our post: Top 11 Data Analytics and Business Intelligence Tools for FinTech). We can gladly help you with development and optimization.