Being agile with Big Data: Overcoming major data preparation challenges and optimizing the pipeline
Many big data analytics companies have seen an exponential growth in recent years and paved the way for upcoming generations to make use of it for their business. According to Statista, the global big data market has generated a revenue of $49 billion in size. Companies have begun to collect large amounts of data in preparation for digital transformation and are looking for data analytics experts. The report drawn from Big Data Analytics in the Retail Market from 2021 to 2028 shows that it has reached a market size of USD 4.56 billion in 2020.
According to the latest analysis by Emergen Research, big data analytics in the retail market is expected to rise at a CAGR of 21.2% in the upcoming years. Although the concept of big data is new and has started to evolve, the origins of large data sets are found in the 1960s and 1970s. It was the period when the world of data just began with the first data centers and the development of the relational database. Later, big data eventually started to shape various IT consulting firms and create more opportunities.
What is Big Data?
Big data is a collection of data that arrives in a huge volume and yet grows with time. It is capable of managing large amounts of data with a complexity that no other traditional data management tools can match to store it efficiently. For instance, the New York Stock Exchange is the best example of big data since it generates one terabyte of new trade data per day. These huge volumes of data are used to address business problems that were not possible to tackle before. Big Data is the major reason behind new business models rising in a competitive world.
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How does Big Data work?
Big data ensures the collection of data from various sources and applications. Earlier, it followed up with traditional data integration mechanisms that did not meet the expectations of finishing the task. They slowly started to strategize and introduce new technologies to manage big data sets at a terabyte, or even petabyte, scale. During integration, the data is collected for processing, formatting, and availability in a form for business analysts to kickstart their work.
The need for big data requires huge storage space, and the cloud is the solution. This enables businesses to bring in the data of their choice and process the data sets on an on-demand basis. The cloud is gradually becoming popular among investors since it helps them compute requirements and allows them to spin up the resources according to their needs.
The investment in big data in today’s world brings more profit when they start to analyze and act on their data. Explore the data regularly to ensure you’re visually up to date on a variety of data sets. Start with the data work after building the data models with artificial intelligence and machine learning.
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How to resolve data preparation challenges and optimize the pipeline?
In the long run, big data plays a major role for many organizations in bringing back changes to their business insights and operational agility. Since big data has some complexity and high costs for deploying and managing the data projects, it’s not leading them to production. To overcome these problems, new big data use cases are adopted for the sheer ongoing maintenance of the pipeline.
Developing a challenging data pipeline:
The challenges of big data analytics are true when it comes from hand-coding to old-fashioned ETL and it’s still changing. Developing challenging data pipelines on a distributed computing framework involves implementing more writing transformation logic in non-distributed, single-server environments. In the modern data world, the data pipeline is built by expertise using different technologies to make it work in a distributed environment.
Debugging the transformation logic:
In recent years, these problems were faced in the old ETL days. The data pipeline holds data from different sources for combination and makes progress after it is normalized and cleansed to flow through the pipeline. Developers are making it possible by debugging the code visually for smooth progress. The additional challenge that comes next requires smart sampling to debug the pipelines for storing data sets across multiple nodes in the cluster.
Creation of repeatable pipeline:
Big Data problems and solutions are faced when writing a pipeline that will run ad hoc queries. Running the production environment requires the same pipeline for dealing with errors, process, performance, and availability issues. The process of handling the constant change of source data requires two distinct pipelines, one for initial data load and the other for incremental loading of data.
Pipeline performance tuning and optimization:
The process of writing a pipeline that runs slowly is more satisfying, and when it comes to tuning the performance, the SLAs have to be processed differently. The challenges faced by the underlying big data pipelines do not perform similarly since they vary with versions and optimizations. Additional adjustments are considered to the computing and storage environment. Last but not least, optimizing and debugging the Spark code is required to maintain the ongoing pipeline.
Maintaining High-speed query performance:
Market-leading business intelligence and data visualization tools are not capable of handling large data volumes that are associated with big data. A strong approach, like creating OLAP cubes and in-memory modules, is required to make the data visualization tools work. To avoid scalability issues, a few use-cases are implemented to access the pipeline directly on big data platforms and increase the performance simultaneously.
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Summing Up :
The right time has come to take the challenges of big data analytics to the next level. The technology will keep rolling every year and requires a huge contributing factor for various IT consulting firms to eliminate the challenges that plague data analysts. If the data is clean, it results in businesses with accurate reports that help them with decision-making. Organizations can start resolving data preparation challenges and optimizing their entire process by availing of the right data analytics platform at an affordable price from a reputed big data analytics company to skyrocket their dream business.