Building a High-Performance Big Data Analytics System

Analyzing data helps you power business growth with informed decision-making.

Analyzing big data, on the other hand, helps you launch targeted marketing campaigns, boost customer acquisition & retention, and develop capabilities to predict the future.

In fact, 44% of organizations used big data analytics to conduct market research last year.

In this blog post, we’ll take a look at the key benefits of a big data analytics system, as well as how you can build one to fuel business success with data-driven insights.

So, let’s get started!

How does a big data analytics system help me?

• Close the gap between ideas and results: Solve complex, non-linear problems, right from strategy to execution.
• Define market trends: Get data-driven insights faster and use them to define market trends.
• Launch targeted marketing campaigns: Provide customized products to your target market. Create targeted campaigns by monitoring online purchases and point-of-sale transactions.
• Create innovative products: Update existing products and create innovative ones, as well. Understand your customer base better by implementing processes to track customer feedback, product performance, and competitors.
• Make actionable recommendations: Align business objectives and data findings to make recommendations that you can execute effectively.
• Develop increased resilience: Use data intelligently to become agile to dynamic market forces.
• Boost customer acquisition & retention: Pinpoint customer needs by observing behavior patterns. Keep customers satisfied by identifying behavior and triggering brand loyalty.
• Identify potential risks: Develop effective risk management solutions. Create informed strategies by increasing the efficiency of risk management models.
• Enhance business performance: Upgrade essential capabilities by building systems to support data analytics.
• Develop capabilities to predict the future: Get immediate results for the issue at hand, as well as insights to predict future challenges and opportunities.

How do I build a high-performance big data analytics system?

There is a range of performance considerations to keep in mind when building a high-performance big data analytics system.

Data Acquisition

This is where data from diverse sources enters your big data system.

• Make sure that data-inflow to your database occurs with intervals, and not at the same time.
• When pulling data directly from an external database, make sure to pull it in bulk.
• Use appropriate parsers when parsing data from a feed file.
• In addition to this, use in-built or out-of-the-box validation solutions. They generally perform faster than custom validators.
• Use built-in libraries and frameworks when parsers or validations aren’t running in the server environment. They will perform faster than custom codes.
• Also, use readily available or built-in transformers.
• Filter out invalid data as early on as you can. This will help you ensure that only legitimate data-sets are processed after validation.
• If you need to cleanse a valid data source, perform this action on most of the data, as opposed to doing it record by record.
• Ensure that you achieve parallelization in data transformation. This step is usually the most resource and time-consuming.

Data Storage

Once all the steps involved in data acquisition are complete, you need to store the data on some kind of persistent storage.

• Your data model impacts database performance, redundancy, as well as disk storage capacity.
• Database configurations and properties such as replication and consistency levels also affect database performance.
• How you configure partitioning will impact the performance of your database.
• Most big data systems have NoSQL databases to store and process large data-sets.
• Properties of NoSQL databases such as level of compaction, size of buffer pools, timeouts, as well as caching have an impact on database performance.
• Furthermore, not all NoSQL databases have built-in support for techniques such as joins, sorts, aggregations, filters, and indexes. If you need to use them, go for a solution that has these features built-in.

Data Processing

This is the core of your big data system where the bulk of data processing takes place. This includes summarization, forecasting, and aggregation.

• Select an appropriate data processing framework after you have evaluated the framework and system requirements in detail. It is important that the framework is capable of meeting your data requirements.
• Make sure to view the number of jobs that you have launched for a given task.
• Keep track of data transfer size for job processing. While data locality will give you improved database performance, getting to a higher level of locality will also mean data replication at multiple locations.
• In addition to this, merge the results of a real-time stream event with the output of batch analytical processes. Design your system in a manner that helps you handle this process smoothly.
• Also, design your data system in a manner that helps you overcome situations in which re-processing on the same dataset may be required. This can occur as a consequence of an error/exception in initial processing.
• Store the final output of processing jobs in the format/model in which you expect results from your big data system.
• Monitor and measure database performance with the tools provided by the data processing framework you are using. This will help you get a sense of how long it is taking to complete a given job.

Data Visualization

This is where you will get a detailed view of processed data.

• Ensure that your data visualization layer displays data from the final output tables.
• Avoid reading raw data from your visualization layer. This will help you minimize data transfer as well as heavy data processing when viewing reports.
• Maximize the use of caching in your data visualization tool.
• Keep pre-computed values in summarized tables. If you need to make calculations at runtime, keep them to a minimum and work on the highest data levels possible.
• In addition to this, keep the size of images such as graphs and charts to a minimum.
• Finally, make sure to plan for resources such as CPUs, memory, disk storage, as well as network bandwidth.

Drive business growth with high-performance big data analytics!

In this blog post, we looked at the key benefits of a big data analytics system, as well as how you can build one to fuel business success with data-driven insights.

Analyzing big data helps you enhance business performance, define market trends, and close the gap between ideas and results.

Make Smarter Business Decisions With a Range of Data Analytics Solutions! Know More.