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August 12, 2020

Process Big Data in Everyday Work


Edwin Lisowski

CSO & Co-Founder

Reading time:

10 minutes

Nowadays, big data is literally everywhere. Most likely, your company utilizes it as well! In this article, we are going to show you some of the most important big data applications in everyday life and work. And there’s more! We will name eight of the most popular big data tools to help you process big data in your everyday work. Let’s just dive in!

Big data is constantly present in our lives. What has started as a technology reserved almost exclusively for advanced IT companies, now is a worldwide standard, utilized in almost every sector, industry, and a sphere of life. And that’s a very positive development! Big data help us work in a more effective way, make more informed decisions, and utilize the information we possess in the best possible way.

Let’s take a brief look at how big data is used in everyday work. We have selected four crucial areas where big data is indispensable.

You may find it interesting – Big Data Consulting Services.

Big Data areas

Banking and finances

Financial companies and institutions utilize a lot of information about customers and the market. These companies know how much money we make, where our money comes from, what we are spending it on, and what kinds of services we use. No wonder that the amount of data generated each second in the banking sector will grow by 700% by 2020[1].

Naturally, banks use that data in order to optimize their marketing activities and introduce more tailor-made offers to the market. But also, and foremost, banking companies use big data in order to detect and prevent any illegal activities, such as various types of frauds and money laundering.

SAS, one of the IT global leaders, has even made their own anti-money laundering software, based on machine learning. According to SAS, their software aids financial institutions in achieving more than 90% model accuracy and a reduced number of false positives by up to 80%[2].


Transportation and logistics

The road operators in various countries use big data analytics in many of their everyday duties:

  • Route planning: Operators use big data in order to understand and estimate road users’ needs on different routes. This comes especially handy during various road works.
  • Congestion management and traffic control: Thanks to big data, avoiding traffic jams is now easier than ever. Just think of Google Maps–this solution is not just the very precise GPS navigation service, but also a traffic monitoring system. If Google Maps detects a traffic jam on your route, it will advise you to take an alternative way in order to avoid it.
  • Increasing safety: The real-time processing big data and predictive analysis helps authorities and road operators identify intersections and places of increased traffic accident risk. This knowledge allows them to put additional signs or rules that help people driving through a given intersection safely.

What about logistics? Admittedly, big data is everywhere in this sector. Warehouse operators, courier companies, airlines, and other transportation companies use big data to optimize their work. For instance, courier companies that use aircraft can schedule flights, predict delays based on weather data, and estimate the demand for storage space in every plane. Also, thanks to these accurate predictions, they can offer parcel tracking and precisely inform about the delivery time.



Politicians use big data to help them overcome national and global challenges and threats, such as unemployment, terrorism, pollution, and many others. Moreover, big data can be used to identify areas that require the immediate authorities’ attention, for instance, local protests or wildfire.

Moreover, big data is broadly utilized during elections. Indeed, big data can influence elections or other vital decisions. Recently, we’ve heard about the high-profile case of Cambridge Analytica. Cambridge Analytica is a British political consulting firm that was founded in 2013[3]. One of the main reasons that this organization is well known is because of the controversy that happened during the presidential election in 2016.

Cambridge Analytica used data from US Facebook users without their consent for political purposes, to help Donald Trump in his campaign. This company also had a great influence on the campaign in favor of the departure of Great Britain from the European Union (Brexit)–their client was Leave.EU, a political campaign group that supported the British withdrawal from the European Union in the June 2016 referendum[4].


Marketing & customer service

Today, every decent marketing agency and e-commerce company uses big data to optimize their campaigns and offer more personalized customer service. Marketing companies, just like financial institutions, have a lot of information about consumers and the market. Just take Facebook–it’s simply an endless source of vital data regarding habits, expenditures, holidays, interests, professions, and beliefs. Facebook has 2.6 billion monthly active users as of the first quarter of 2020. That makes it the biggest social network in the world[5].

E-commerce companies use transactional data to understand customers’ purchase history. As a result, these online stores can offer more precise product recommendations or attractive discounts. And tracking each user’s activity in a store is a basis for the remarketing strategy, which allows you to show ads of the product that your customer is already interested in, increasing the chances that eventually they will buy it.

As you can see, big data is everywhere around us. With this foundation developed, we can switch to our list of tools that can help you process big data.


Useful tools to process big data

We decided to show you eight of the most popular tools to process big data.

Process Big Data: Apache Hadoop

This is one of the most popular big data tools. It’s a software library and a framework that allows for the distributed processing of large datasets, using simple programming models. It was designed to handle even thousands of machines, each offering local computation and storage. Their HDFS (Hadoop Distributed File System) can work with different types of data–text, video, and image files. What’s also important–this software is free to use (it’s under the Apache License)[6].

Process Big Data: Cloudera data platform (CDP)

You can think of it as of extended Apache Hadoop, on which it is built. You can think of it as of extended Apache Hadoop, on which it is built. CDH was explicitly created to meet higher enterprise demands. This platform comprises Apache Hadoop, Apache Spark (the open standard for in-memory batch and real-time process for advanced analytics), Apache Accumulo (it’s a secure data store to serve performance-intensive big data applications), and many more elements and tools. CDH allows you to collect, process, discover and distribute almost unlimited volumes of data. Just like Apache Hadoop, it’s free to use.

Process Big Data: Apache Cassandra

As Apache assures, it’s the perfect platform for mission-critical data. It’s an open-source distributed NoSQL DBMS constructed to manage huge volumes of data spread across numerous servers. One of the crucial advantages of Cassandra is that it handles large volumes of data with no loss in performance. It offers log-structured storage, and–as pervious tools–it’s free to use.

Process Big Data: Apache Storm

It’s a free and open-source computation system. Storm allows you to process unbounded streams of data. What’s vital for many users is that this platform is simple to use and can be combined with any programming language. Moreover, it integrates with the queueing and database technologies you already use. Apache Storm is used primarily for real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. It can be easily used for large-scale projects, although many people say it’s difficult to master.

Process Big Data: KNIME

KNIME Analytics Platform is the open-source solution for data-driven innovation, designed for discovering the potential hidden in data, mining for insights, or predicting outcomes. Thanks to various commercial extensions, companies can adjust it to their needs and achieve expected results in a much faster, optimized way. The abbreviation KNIME stands for Konstanz Information Miner. This tool is especially used for reporting, integration, research, CRM, data mining, data analytics, text mining, and business intelligence. It offers simple ETL operations and seamless integration with other technologies.

Process Big Data: Lumify

It’s a free and open-source platform used for big data fusion, analysis, and visualization. Lumify enables users to discover connections and relationships in their data through a suite of analytic options, including graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces shared in real-time. Lumify works on Amazon’s AWS environment and runs on most cloud environments built on the premise[7].

Process Big Data: Talend

Talend Open Studio for Big Data helps you in work with your big data thanks to a drag-and-drop interface and pre-built connectors and components. Because Open Studio for Big Data is fully open-source software, you can see the code and work with it. One of the essential advantages of Talend is its performance–allowing you to work in real-time.  Within Talend, you also have several other tools and platforms:

  • Open Studio for Data Integration
  • Open Studio for Big Data
  • Data Preparation–Free Desktop (enables users to discover, blend, and clean data)
  • Open Studio for ESB (allows you to seed up orchestration of applications and APIs)
  • Open Studio for Data Quality (helps in assessing the accuracy and integrity of data)
  • Stitch Data Loader (enables you to load data from various sources into cloud data warehouses and data lakes)

Process Big Data: Datawrapper

This platform is used primarily for data visualization purposes. Datawrapper aids users in generating simple, precise, and embeddable charts, and do it very quickly (for instance, you can simply upload an XLS/CSV or Google Spreadsheet file. It allows you to choose one of 19 interactive & responsive chart types ranging from simple bars and lines to arrow, range, and scatter plots. Datawrapper is mostly used by media companies to present statistics in an attractive, interactive way. The simplest Datawrapper plan is free, with limited customization options.

If you are interested in implementing big data analytics in your company – drop us a line! Addepto comprises experienced big data specialists, as well as other AI-related experts. With our help, you can master how to process big data, and take your company to a whole new level!



[1] Andrew Zangre. 44 Noteworthy Big Data Statistics. Mar 20, 2019. URL: Accessed Aug 12, 2020.
[2] Sas. SAS® ANTI-MONEY LAUNDERING. URL: Accessed Aug 12, 2020.
[3] Wikipedia. Cambridge Analytica. URL: Accessed Aug 12, 2020.
[4] Wikipedia. Leave.EU. URL: Accessed Aug 12, 2020.
[5] H. Tankovska. Facebook: number of monthly active users worldwide 2008-2020. Feb 2, 2021. URL: Accessed Aug 12, 2020.
[6] Apache. Apache Hadoop. URL: Accessed Aug 12, 2020.
[7] Altamiracorp. LUMIFY SLICK SHEET. URL: Accessed Aug 12, 2020.


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