Big data is a term that defines the large volume of data sets – both structured and unstructured having variety and complex structure with challenges, such as difficulties to capture, store, analyze, visualize and process data. It requires new Big Data tools and techniques, architecture solutions to extract and analyze data for insights that lead to better decisions and strategic business moves.
The increasing Volume of Business Data
- Data is growing at a rapid pace. By the end of 2020, 44 trillion GB of data would be accumulated.
- Facebook users send around 31.25 million messages and watch 2.77 million videos every second.
- 40,000 search queries are performed on Google per second. It accounts to 3.46 million searches every day and 1.2 trillion each year.
Examining large volumes of data may be a challenge for most organizations from different industry verticals. Big data analytics can help businesses get clear and useful insights from today’s large diversified data sources. Cloud applications, social media, and machine sensor data are a few examples. The concept of big data has been around since the last few years and small and large businesses have already adopted advanced big data analytics to uncover insights and trends to gain a competitive advantage.
Data produced by organizations have a specific structure. Companies need to organize the data to utilize it.
Big data concepts can be used to manage:
- High volume – lots of data
- High velocity – data arriving at high speed
- High variety – many different data sources and formats
- Veracity – quality of captured, affecting the accurate analysis
Big data analytics processes involve collection, organization, and analyzing large sets of data to extract various types of useful information from it. This futuristic technology helps analysts identify different patterns of data and understand the information contained within it. This helps organizations make better decisions.
Big Data Techniques
Big Data needs extraordinary techniques to efficiently process a large volume of data within limited run times. There are many specific techniques in these disciplines, and they overlap with each other too.
Optimization Methods can be used for solving quantitative problems in different sectors such as biology, economics, and engineering.
Statistics involves the collection, organization, and interpretation of data. Statistical techniques are used to describe the correlation between different objectives.
Data Mining is a technique that is used for extracting valuable information from data. It involves clustering analysis and classification.
Machine Learning is used for designing algorithms that help systems evolve different behaviors and businesses can make intelligent decisions.
Artificial Neural Network (ANN) is an advanced technique that is found in pattern recognition, adaptive control, image analysis and more.
Visualization Approaches are useful to create tables, diagrams and other representations to understand data.
Social Network Analysis (SNA) is an important technique that is used in modern sociology, viewing social relationships and involves nodes and ties also.
Higher level Big Data technologies include distributed computational systems, file systems, data mining, cloud-based storage, and computing.
An Example of Big Data Basics and Analytics
For example, a company runs big data analytics on the past sales data. The sales teams get clear insights into the buying patterns of the customers in different regions. They can plan the best strategies for marketing and advertising based on the trends followed by the customers. Predictive analysis of the data helps businesses maximize their sales and profits.
Big data technologies help businesses to get insights from today’s huge data resources. People, organizations, and machines now produce massive amounts of data. Social media, cloud applications, and machine sensor data are just some examples. Big data can be examined to see big data trends, opportunities, and risks, using big data analytics tools. With the help of advanced and sophisticated software programs, big data analytics converts unstructured data into structured one to reap several business benefits. Data scientists and predictive modelers can uncover unknown correlations, customer behavior, and market trends by using big data technologies.
Also Read: How big data and ai will impact software development
Types of Big Data Analytics Tools
Big data analytics tools help enterprises and companies to manage big volumes of data generated by different processes. There are thousands of big data tools that can help you save time, money, and provide valuable business insights.
Apache Hadoop is an open-source Java based big data analytics framework that is used by a lot of large corporations. It is known for its great capabilities and the ability to handle unlimited tasks or jobs. Businesses can manage processing of voluminous data sets using effective programming models and scale your data up and down without worrying about any hardware failures.
Cloudera is a brand name for Hadoop with a few extra services. Cloudera is an enterprise solution to help businesses manage their Hadoop ecosystem better. Businesses make use of Cloudera to create a data repository that can be accessed by corporate users for various purposes. Transform your business processes and reduce the risks in order to gain a competitive advantage.
“Big data applications are analytics is projected to grow from $5.3B in 2018 to $19.4 B in 2026.”
It is a modern alternative to databases that help you manage data that changes frequently. If the data is semi-structured or unstructured, then MongoDB can help to store data from mobile apps, product catalogs, content management systems, and more. MongoDB is not meant for a data newbie. You should know how to query it using a programming language.
It is one of the pillars behind Facebook’s huge success. Apache Cassandra allows users to process structured data sets that are distributed across a huge number of nodes worldwide. Cassandra is a popular database that offers high availability and scalability and enhancing the performance of the hardware and cloud infrastructure. Some of the major advantages of Cassandra include high performance, fault tolerance, decentralization, durability, and exceptional support.
Known as a distributed data management for Hadoop, Hive supports SQL-like queries for accessing big data. Hive is used for data mining purpose.
It is an alternative to Hadoop that is slightly different from Hadoop. It helps companies run MapReduce jobs quickly. Apache Spark opens up new opportunities for streamlining data processes. Fraud detection, log processing, and trading data becomes easier with Apache Spark.
Any big data tool list is incomplete without Apache Storm. Businesses choose Apache Storm for processes that involve real time results. If you are looking for accuracy and immediate results, you can rely on Apache Storm. One can integrate Storm with Hadoop or use it alone to streamline the processes.
Talend is a great open source company that is known for providing various data products. No matter what stage of business you’re into, you can use Talend to maintain your own data management system – which may be a complex and difficult task.
Big data Analytics Lifecycle
A step-by-step methodology is required to organize the activities and tasks that are involved in processing and analyzing activities. Right from Big data adoption and planning, the entire process should be planned.
Benefits of Big data analytics for modern enterprises
Organizations can analyze their data in full context quickly using Big data, and also analyze it in real-time. With high-performance data mining, forecasting, and optimization, companies can use big data analytics to drive innovation and make better business decisions. Enterprises can narrow down the most relevant information and improve customer retention. It improves operational efficiency and reduces the risks.
Cloud-based analytics and Hadoop are some of the popular big data tools that bring cost advantages to enterprises. These high-speed tools are capable of analyzing data immediately and help you make quick decisions. If you haven’t adopted this technology yet, it’s time to look for the best big data analytics service provider to help you get the best solutions.