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      Big Data Analytics

      Building a High-Performance Big Data Analytics System

      May 27, 2020

      4 minute read

      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?

      Building a High-Performance Big Data Analytics System
      • 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?

      Building 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.
      Learn more about the differences between SQL and NoSQL databases with this blog post.

      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.

      At Grazitti Interactive, the data analytics team knows just how important it is to be able to drive decision-making with big data analytics. Should you want to know more, write to us at info@grazitti.com and we will take it from there.

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