Big data, as the name suggests, is a collection of data in massive volumes that grow exponentially with time. The data is of a larger size and complexity that traditional data management tools cannot process or store efficiently. Giants like Amazon and Facebook, along with organizations like the stock exchange use big data to analyse their user base and devise business strategies according to the input they receive.
Big data can be divided into Operational big data and Analytical big data technologies. Operational big data refers to the volume of data generated every day, from a particular company that is used for analysis by the software used for big data technology. The operational data such as social media or transactions act as raw data to provide analysis. Analytical Big Data Technologies refers to the advanced adjustment of Big Data Technologies. This contains the analysis of big data that is instrumental to big business decisions for example stock markets, medical records, weather reports among others. Big data development in this context means the overall analysis of raw data collected from various sources.
In the current pandemic, companies have become more reliant on big data services to allow them to take business decisions faster and efficiently. It is not possible to analyse big data with the help of traditional data management tools and hence, it is imperative for companies to resort to professionals who deal with big data solutions on a regular basis to provide an accurate analysis. Businesses can greatly benefit from big data analysis as it can enable companies to increase revenue, reduce operational risks, increase sales, prevent equipment failure, reduce downtime saving costs in the long run, enhance customer satisfaction and efficiently flag fraudulent and suspicious behaviour towards their establishments.
The big data services use the analysis of trends to reach conclusions in their analysis. There are certain trends prevalent in big data development underlined below:
- Real-time analysis: Big data developers who are proficient in Kafka and Kinesis are a business’ best bet when it comes to real-time analysis. In such uncertain times, companies rely on real-time analysis to guide their way through the economy. An instant reaction help companies boost sales and flag suspicious behaviours in order to minimise their losses if any.
- Automation of Data Management: To undertake the herculean task of analysing big data, companies that provide big data solutions rely on AI or machine learning to automate the detection of any inconsistencies in an organisation’s data. The AI also helps businesses fill in gaps in their data as well as remove data duplications for better analysis.
- Data Warehousing: To establish data security, specialists providing big data services practice the secure storage of data with periodic augmentation. They also archive data from multiple sources providing business intelligence provisions for the organization.
- Cloud agnostic for big data development: Using multi-cloud, hybrid-cloud and cloud-agnostic to store data, the services decrease the dependency on one cloud vendor. This allows organisations to explore a more efficient form of cloud storage and enjoy scalability optimising the cost of big data development in the future.
While big data might look like just data interpretation on the outside there is a definite set of skills that are required in the industry. Big data technology is a complicated realm and becoming a data analyst needs a person to be abreast with the following skills:
Having a passion for coding is essential for a big data analyst. Big data technologies require conducting numerical and statistical analysis with large volumes of data. Learning how to code can help you master the science of data analysis in large volumes. Some of the languages which are recommended to learn are C++, Python, R and Java with Python being a primary focus on the list.
Interacting with databases with queries and statements will also help a young mind think analytically. Tools like SQL, HIVE and Scala should be a part of your arsenal when you are looking to dive into big data technology.
Without quantitative skills, programming skills will not serve an individual for very long. Multivariable calculus, linear and matrix algebra is recommended to get you started with quantitative skills. A developer also needs to be well-versed with statistics. With a strong balance of numerical and quantitative skills, learning the newer approaches to big data like machine learning are not hard to pick up.
Learning Multiple Technologies
Big data as an industry is continuously evolving with new routes of data coming to the fore. Deep knowledge of basic tools like Microsoft Excel, SQL, Linux, Hadoop and HIVE is crucial to learning and analysing big data. The technologies used by various companies differ according to company policy however, to ace big data solutions, a rudimentary knowledge of traditional technology is a necessity.
Business acumen and understanding outcomes:
Expertise in the domain that the big data analysts are operating in is a must-have skill. Without the ability to gauge the data and understanding the outcomes of a particular data set, it is not possible to predict potential business trends and opportunities. Business acumen will also result in an informed decision-making process when it comes to leveraging the data to drive company sales.
Interpretation of Data:
Big data analytics require individuals to have a curiosity in the company data and the reason behind trends falling or rising. Interpretation of data helps an organisation predict whether they can leverage their data to their profit or not. Relying on pre-determined systems without diving deep into the interpretation can lead to an ineffective business model. It is important to be abreast of the latest developments in big data starting with the top frameworks used by big data solutions. These top 7 frameworks are used in the IT industry and are booming in the year 2021:
- Hadoop Ecosystem:
Developed to store and process data with a simple programming model, the Hadoop ecosystem operates in a distributed data processing environment. It allows big data services to store and analyse data at both high-speed as well as low-expense machines. Hadoop has been a popular choice among Big Data technologies. A lot of features for Hadoop still remain unexplored and show great promise in the future.
- Artificial Intelligence:
Artificial Intelligence is creating ripples in almost all industries. AI provides a broad bandwidth of technology to deal with the development of intelligent machines capable of carrying out different tasks typically requiring human intelligence. AI is revolutionizing big data development by providing advanced technology at the tip of your finger.
A document-oriented distributed database, MongoDB is aiding the data management of unstructured or semi-structured real-time data for application developers. MongoDB is an open-source data analytics tool that is being used to create innovative services on a global scale. With a multi-cloud database called MongoDB Atlas, the database helps to store data in JSON-like documents allowing dynamic schemas. The multi-cloud database Atlas provides automation along with elasticity, scalability as well as the availability of data consistently.
- NoSQL Database:
The NoSQL Database has a wide variety of big data technologies developed to design modern applications. It provides data acquisition or data recovery through a non-SQL database. The database stores unstructured data and offers flexibility while addressing various data types. It also provides easier horizontal scaling, control over devices and design integrity.
Through Qlik, big data development can gain transparent raw data with efficient integration and automatically aligned data association. Qlik helps to detect potential market trends by integrating embedded and predictive analysis of data. It also supports real-time data with a multi-cloud structure and an associative engine. The associative engine delivers unlimited permutations of big data analysing and indexing every relationship inside the data.
- R Programming
Another open-source big data technology is R Programming which is widely used for a unified development environment, statistical computing and visualisation. Experts have observed R to be a leading language in big data technology on a global scale. It is also widely used by data miners as well statisticians to analyse and develop statistical software.
RapidMiner delivers transformative insight to big data technology and analysts. With the help of Rapidminer, organisations can upskill efficiently. Providing predictive analysis, data preparation, text mining and deep learning, the platform is emerging to be a popular choice among researchers and people who are not professional programmers. It also allows users to load real-time data providing great insights into company data collection.
As the world changes and technology evolves, big data plays a big role in 2021 when it comes to security and progress. Through data collected from various sources better healthcare, traffic control and security in data sharing can be increased. Businesses can use big data to navigate through the economy bringing more sustainable products and services. The emergence of new tools is due to security breaches as the world becomes smaller and more connected causing a bigger ripple effect whenever there is a technological faux pas. Big data is maintained to ensure that data is efficiently tracked over its lifecycle.