Definition of Big Data Analytics: Big data analytics is the process of understanding and examining varied and large data sets. These large data sets are called big data and they are used to uncover some unknown correlations, hidden patterns, customer preferences, and market trends.
Definition of Big Data: Earlier, data was collected using some applications or methods since there was only so much that one had to collect. Now, data is being collected every second of every day, which makes it hard to use traditional applications and methods to collect that data. This data, because of its large volume, is known as big data.
Big data will help an organization make some informed decisions for the business. Data analytics techniques and technologies provide users to analyze large data sets and draw some conclusions about them. This will help the business make informed decisions. You can answer some business intelligence queries to improve the performance and operations of the business. Big data analytics involves the use of complex applications that include predictive models, what-if analysis, and statistical algorithms. These applications are powered by analytics systems.
Why Big Data Analytics important?
Big data analytics is performed using specialized software and systems. This type of analytics offers numerous benefits, including:
- More effective marketing
- Better customer service
- Improved operational efficiency
- New revenue opportunities
- Competitive advantages over rivals
Predictive modelers, data scientists, statisticians, big data analysts, and other analytics professionals use big data analytics applications to analyze the large volumes of structured data and other forms of data that are not tapped by other analytics or business intelligence programs. This data is a mix of unstructured and semi-structured data, including web server logs, Internet clickstream data, text from customer emails, survey responses, machine data, and mobile content and social media content. This information is collected through some sensors connected to the IoT or Internet of Things. Big data analytics is a type of advanced analytics, which is very different from traditional business intelligence.
What are the tools and technologies for it?
Semi-structured and unstructured data types do not fit well into a data warehouse. These warehouses are based on relational databases that only work well with structured data sets. A data warehouse cannot handle processing demands posed by large volumes of data, especially when these data are frequently updated. This is the case with real-time data on online activities of visitors on a website, performance of mobile applications, or stock trading.
As a result of this, numerous organizations have started to collect, process, and analyze data, and they do this using Hadoop and NoSQL databases. They also use numerous data analytics tools like:
- MapReduce: This software provides a framework that allows a developer to write code or scripts to process large volumes of semi-structured or unstructured data across stand-alone computers or numerous processors.
- HBase: This tool is a value or key oriented data store that is built to run only on HDFC or Hadoop Distributed File System. The data in this tool can only be stored in the form of columns.
- YARN: This application can be looked at as the second-generation Hadoop, and is used to manage clusters in data sets.
- Pig: This is a technology that is open source and also offers a high-level mechanism that allows parallel programming. It enables MapReduce to execute numerous applications or programs on Hadoop.
- Hive: This is a data warehouse system that is open source. It is used to query and analyze large data sets that are stored in Hadoop files.
- Spark: This tool provides analysts with a framework that enables them to process numerous scripts or programs in parallel. It also enables users to run a large-scale analytics application across numerous systems.
- Kafka: This tool is a messaging system that is designed to replace messaging systems used by brokers.
How Big Data Analytics works?
NoSQL systems and Hadoop clusters are often used as the staging areas or landing pads for data before it is loaded into any analytical database or data warehouse for analysis and storage respectively. This data is loaded into these systems or clusters in a summarized form that is easier for the machine to understand since it is in the same format as a relational database.
Big data analysts and users often adopt the concept of a data lake in Hadoop clusters. This data lake serves as the primary repository for any raw data that is collected by the system. The data is analyzed or run directly through a Hadoop cluster or Spark, a processing engine, respectively. Like every other data analysis process, it is important to remember that the data needs to be cleaned and processed when it is collected so it can be used for analysis. Any data being stored in HDFC should be cleaned, organized, configured, and partitioned properly so it is easy for data analysts to extract, transform, and load any analytical queries and integration jobs.
When the data is ready, a data analyst can analyze it using some commonly used software for some advanced analytics processes. These types of software include some tools for:
- Predictive analytics that helps analysts build models that can forecast any future developments or customer behavior
- Data mining which allows analysts to sift through large volumes of data to look for relationships and patterns in the data set
- Machine learning that allows analysts to use different statistical and mathematical algorithms to analyze large volumes of data
- Deep leaning which is an advanced form of machine learning
Some statistical analysis and text mining software also play an important role in the analytics process of big data. You can also use some data visualization tools and mainstream business intelligence software for the same. For both analytics and ETL applications, you can write queries in MapReduce using some programming languages like Scala, SQL, R, and Python. These are the standard languages for relational databases that are supported on numerous Hadoop platforms.
What are the challenges and uses of Big Data Analytics?
Every big data analytics application will use data from both internal and external sources like demographic data regarding customers, weather data, and more. Some of this information can also be sourced from third-party applications or service providers. Additionally, some applications are being built to source real-time data so that analysts can perform real-time analytics on data that is fed into applications and cluster systems like Hadoop. This can only happen through stream processing engines like Storm, Spark, and Flink.
Big data systems of the past were deployed based on the need of the organization. These organizations often collected, cleaned, organized, interpreted, and analyzed large volumes of data. Microsoft, Amazon Web Services (AWS) and other cloud platform vendors have made it easier for organizations to set up and manage data in the cloud using Hadoop clusters. Suppliers of Hadoop like Cloudera-Hortonworks that supports the distribution of large volumes of data in Microsoft Azure and AWS cloud services. A user can now spin these clusters in the cloud, run them for as long as possible.
Supply chain analytics and businesses in other industries have begun to use big data analytics. In supply chain analytics, the software uses both quantitative and big data methods to make it easier to process the data across the supply chain. Big data analytics in the supply chain expands the data set, which allows the business owners to learn more from the data since they can improve their analysis. Some issues with big data analytics are that companies find it difficult to hire experienced data engineers and scientists who can fill the gaps. Companies also find it difficult to analyze the data since the tools and software are expensive. Additionally, not every company has the right resources to perform some internal analysis.
History
It was in the year 1990s that the term big data was used for the first time. This term was used to describe the large volumes of data being collected. Doug Laney, an analyst at Meta Group Inc., expanded the definition of big data. He ensured that people were aware that big data also includes a variety of data. This data is generated by large businesses frequently, and the speed at which this data is generated is unimaginable. This is when the three factors, called the important V’s of big data, volume, variety, and velocity were popularized.
Hadoop Distributed Processing Framework, HDFC, was launched as an open-source project named Apache in the year 2006. This planted the seeds to build a clustered platform to run some big data applications. By the year 2011, most organizations began to look at big data analytics, and numerous applications like Hadoop and other big data technologies developed to cater to the increasing need.
Before the Hadoop ecosystem started and the framework began to mature, big data applications were taken care of by e-commerce and large Internet companies like Facebook, Yahoo, and Google. This also included some marketing and analytical services that are required to interpret the data. Since financial service firms, retailers, healthcare organizations, energy companies, manufacturers, insurers, and other enterprises have only started using big data analytics now to understand their customers better.
Big Data Analytics has gained immense popularity over the last few decades. Numerous stories have paved the way for the general public to be aware of the critical shortage of data scientists and also the Silicon Valley valuation bubbles.
