In today’s digital world where data is being generated at an unprecedented rate, organizations must prioritize the management of big data. To achieve this, businesses need to clearly understand all its key components. In this article, we will discuss the components of big data and their importance in the big data ecosystem.
The main components of big data include data sources, data storage, batch processing, stream processing, real time message ingestion, machine learning, and analytics and reporting. Understanding these components is essential for making informed decisions and gaining a competitive advantage.
Big data advantages are enormous and can give your organization a competitive edge for years to come however big data management’s road is full of barriers that you carefully need to plan for such as implementing best practices, following up with the latest big data topics, and achieving the perfect architecture through big data elements to increase the success of the implementation phase.
Big Data Ecosystem Components
Using big data to position your organization for the future is not a simple task and usually requires a lot of preparation stages. Before you can start analyzing data with advanced BI tools, data must first be consumed from sources, translated, stored before being finally presented in a comprehensible fashion. That is why it is important to fully understand, in my opinion, the three V’s of big data.
Big data architectures include some or all of the following components:
1- Data Sources
Data sources are considered the biggest component in big data because they represent the building block for future data analysis. When the data sources are accurate, more meaningful insights can be extracted. These insights, in turn, can help decision-makers make better choices, leading to more positive outcomes.
There are different types of sources that you can have in your organization including databases, data lakes, data warehouses, and social media platforms. These are unstructured data and massive in volume making it difficult to process with the traditional analytical methods.
2- Data storage
Businesses need to store the data somewhere before being processed, and the ideal location for it is typically a data lake, which is a big scalable unstructured database capable of holding a huge number of differently formatted files.
Organizations should make sure that data stored on-premises are properly secured to minimize the risk of data breaches and cyber-attacks. In addition, scalability is one of the most important factor to think about as you can’t foresee the size of data that you will be storing.
Failure to do so will need a significant amount of work to move data from one data store to another, which you should avoid by selecting the optimal storage device for your business.
3- Batch processing
Batch processing is waiting for a particular quantity of raw data to be obtained before performing an ETL job to filter, aggregate, and prepare massive volumes of data for analysis. It is utilized when data freshness is not a problem. Hadoop open-source frameworks are a common alternative for such large data processing.
It is commonly used for data warehousing, report generation, and large scale data analysis that doesn’t require real time response and data to be available. In some cases, batch processing can’t be used in applications where real time monitoring and instant responses are needed.
4- Stream processing
This specific component is responsible for the continuous flow of data which is necessary for real time data analytics. It usually do this by locating and pulling data as soon as it is generated and push it to other components for real time processing.
This could be helpful in a number of use cases like financial applications however it requires more resources and computing as it is constantly monitoring for changes in different data sources.
5- Machine learning
Machine learning is an essential component and technique used to help extract insights and identify patterns from a large, complex datasets. The more data you have stored, the more the algorithms become accurate and helpful over time. These algorithms require a huge number of data to be trained on.
With this technology, it has become easier for us to analyze vast amounts of data by automating the process of finding patterns and relationships. Prior to ML introduction, it was difficult to find hidden insights as it has solved various problems including predictive analytics, image and video processing, natural language processing (NLP), and anomaly detection.
In summary, machine learning is a component of big data because it is a powerful tool that enables organizations to extract valuable insights from massive amounts of data, leading to improved decision-making, enhanced customer experiences, and increased business efficiency.
6- Analytics and reporting
Most big data solutions aim to provide users insights into the data through reporting and analysis. The design may incorporate a data modeling layer, such as a multidimensional OLAP cube or tabular data model, to enable users to study the data.
All of these elements work together to provide customers the opportunity to quickly evaluate data via self-service BI or conventional solutions, slicing and dicing data to unearth potent insights that may help drive corporate operations more efficiently and boost agility.
