Top Big Data Technologies: A Beginner's Guide

Big Data Technologies: Understanding The Overall Concept

The big global company named Amazon has millions of users visiting the site every second. Not only that, billions of people click on a product or other things, and order every single moment. Imagine a notebook or spreadsheet (Excel) keeping that data; wouldn’t it crash? Therefore, to handle this kind of vast data, there are tools used called big data technologies.

What are The 5 Vs of Big Data?

• Volume: What is the amount of data that is generated and also getting piled up? Terabytes to petabytes of data.
• Velocity: How fast data is generated, and it is used.  
• Variety: Data has different shapes, such as text, images, videos, audio files, and numbers.
• Veracity: Can the data be trusted? Is it correct and can be used?
• Value: Is the data collected useful? If yes, then how can it be helpful for businesses to make better decisions and improve their results?

Core Layers of Big Data Technologies

Sources and Ingestion Layer: Collecting Data

This is the layer that gathers information from different layers, and for processing, it sends it to the big data system.

Data Storage Layer: Storing Data

Data is collected; now it needs space to live. So, large amounts of data are stored by the storage layer. After this, the data can be accessed and utilized at any point in time.

Batch and Stream Processing Layer: Cleaning and Processing Data

Raw data that is stored cannot be used immediately. Why? Because it may have errors, duplicate entries, or information that is not required at all. Then the processing layer steps in to clean the data, organize it, and analyze it before someone attempts to use it.

Serving and Analytics Layer: Analyzing Data

After the data is processed, it can be analyzed, and this layer makes processed data available for quick searches, business reports, and machine learning applications.

Data Visualization and Consumption Layer: Showing Results

This layer helps convert the data into easy-to-understand charts, dashboards, and reports for businesses to make decisions.

Data Governance and Security Layer: Keeping Data Safe and Accurate

This layer makes sure that data remains secure, accurate, organized, and accessible only to authorized people.

What Are The Different Big Data Technologies Types?

How data is created, how data is brought in, Where does the data live, how data is made ready to use, analyzed, and visualized, different big data technologies perform different work. They are categorized into different categories based on what they do.

Data Storage Technologies

Companies store large amounts of data either in one place or on multiple computers. Companies use data storage technologies to do so. With the help of these tools, it is easier to save any information, access it, and also manage it at any time.

Popular tools include:

Apache Hadoop (HDFS)

Vast amounts of data can be divided into small portions and saved across multiple systems. This is the tool that helps companies do so.

MongoDB

NoSQL (non-relational) - How information is stored in JSON file, same like that with the help of this tool data can be stored in flexible documents. Not like how data is stored in Excel, which has fixed rows and columns.

Apache Cassandra

Huge amounts of data can be stored and also managed across multiple computers or locations. Even if a computer stops working, this tool is designed in a way that makes the data available. Cassandra helps to fetch the data if one computer fails or the data centre stops working. Therefore, the website keeps working without interruption. In addition, if data keeps growing, more and more computers can be added with the help of Cassandra. So, it becomes easy for companies to handle augmented data.

Snowflake, Amazon Redshift, and Google BigQuery

Companies can store their data online and access it anytime. These tools are cloud-based. Therefore, businesses don’t need to invest huge amounts of money in buying physical servers and storage equipment to store large amounts of data.

Data Processing and Ingestion Frameworks

Apache Spark

Multiple computers can be used at the same time to process and analyze data in huge amounts quickly. Apache Spark is a free, open-source tool that helps businesses with this.

Apache Kafka

Once the data is generated, this tool helps companies collect it and transfer it from different sources.

​Apache Flume

Different activities take place on websites and applications every second. This tool helps companies to collect and transfer massive amounts of log data.

Apache Flink

Data arrives continuously, and with the help of this tool, companies can process that data in real time. This tool is useful for tasks such as monitoring online transactions.

Data Querying and Analytics Engines

These tools are helpful when a company wants to go through large amounts of stored information, notice patterns that are useful for them, and understand what the data says or reveals.

Popular tools include:

Apache Hive

Using commands similar to those used in SQL databases, this tool helps businesses search for data stored in large amounts and also analyze it.

Presto

Because of this tool, information stored in different data sources can be searched and fetched quickly.

Altair RapidMiner

This tool can help to build models which can predict what are the possible future outcomes, and also analyze information.

Databricks

This tool provides a platform where teams can work together to process large amounts of information, analyze it, and build machine learning models.

Data Visualization and Business Intelligence Tools

If complex information can be turned into easy-to-understand charts, graphs, reports, and dashboards, these tools can help with that. Businesses can understand their data without having to go through large spreadsheets or numbers.

Popular tools include:

Tableau

Companies can drag and drop information, and charts, graphs, and dashboards can be created from it. Users don’t need to have any skills related to advanced programming.

​Looker

Looker is a business intelligence tool that helps companies understand their data by creating reports, charts, and dashboards. It allows businesses to track important information, such as sales, customer activity, and overall performance, in one place.

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Use cases of Big Data Technologies

Financial Services and Banking

If someone uses their debit card to purchase something that is unusual, and that too in another country, banks can automatically detect it. Banks can also identify if any unusual transactions are done. Banks can block the transaction for some time to protect your money. Moreover, as banks can see your income, expenditure, savings, spending habits, and repayment history, on the basis of all these, they offer you a certain loan so that you can repay it comfortably.

Retail and E-Commerce

Online shopping platforms and entertainment platforms observe your habits, such as which products you purchase often or look out for, which shows or movies you see, depending on these similar products, and also the same type of shows and movies are suggested to you. With the help of big data technologies, businesses can understand which products are getting out of stock quickly, so that they have enough stock to avoid shortages.

Healthcare and Life Sciences

To track someone’s heart rate, blood pressure, and other health conditions, doctors use various equipment, including wearable devices and health-monitoring equipment. If there is any usual reading, doctors can immediately receive alerts and provide attention to the medical condition before it worsens.

In addition, with the help of big data technologies, healthcare organizations can analyse patient records, geographical information, and disease reports to identify patterns and predict where an illness is communicable. It is a great help to prepare medical supplies, vaccines, and healthcare facilities beforehand.

Conclusion

Companies don’t have to depend only on hardware to store their data. They can store data online. In fact, there are tools that make data ready to use by collecting it, storing it, cleaning it, making it ready for analysis, and creating charts. Many industries, from banks to shopping platforms, use big data technologies to detect fraud and understand customers.

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FAQ’s

1. What is the future of big data?

In the early days, do you know, businesses used to depend on people to go through large amounts of data and at the same time make reports. However, as AI stepped in, alongside cloud computing and real-time processing, the time to analyze data has become shorter for businesses. In fact, businesses don’t need to spend a hefty amount of money on hardware; they can store data online.

Therefore, the future of big data looks promising, making data analysis quicker. Moreover, it will also reduce manual effort.

2. Name 7 characteristics of big data?

Velocity, volume, variety, veracity, value, variability, and visualization are included in the 7 characteristics of big data.  

3. How much demand is it for big data?

Businesses on a daily basis generate huge amounts of information. So, they will also need better ways to manage it, and understand it. Furthermore, the increasing use of Artificial Intelligence (AI), cloud computing and digital platforms has created a higher demand for professionals who can collect, process and analyze massive amounts of data. 

With more companies using data to improve their operations and stay competitive, the demand for big data technologies and professionals with the right skills is likely to remain strong.

4. Will AI replace big data engineers?

It is definitely not the case that AI will replace big data engineers. But it's changing how they work. Artificial intelligence can help engineers automate routine jobs, write simple code, find errors and process data faster. Businesses still need skilled engineers to design data systems, solve complex technical problems, ensure data quality and make important decisions.

For instance, artificial intelligence can help build a data pipeline, but an engineer is needed to check it works and meets the company's needs. Big data engineers who can also use AI tools, along with their technical skills, will have better opportunities in the industry in the future.