Why is big data so important?



 At the beginning of the 21st century, the explosion of relational databases, web access, wireless and other technologies made the study and management of large datasets a real and current challenge that required a new designation. The term “big data” entered the 2013 edition of the Oxford English Dictionary (OED), but this term has been used since the Second World War to convey the notion of exploiting huge volumes of material.

The future of big data essentially refers to datasets that are too large and too complex for traditional data processing and data management applications. Big data has become very popular with the advent of mobile technologies and the Internet of Things (IoT), and it is the result of user activities that generate more and more material (geolocation, social networks, fitness or shopping, etc.) and who access digital data on their mobile devices at least 150 times a day!

It has also become the catch-all term for the collection, analysis, and exploitation of massive volumes of digital material to improve business operations. The cloud is increasingly moving big data and its processing as the volume of datasets continues to grow and applications are in high demand in real-time.

Why is big data so important?

Consumers live in a digital world where their expectations must be met immediately. From digital business transactions to marketing feedback and targeting, cloud businesses are changing very quickly. These numerous and rapid transactions generate and compile data at a breakneck pace. Routinely analyzing this type of information is often the difference between having the information needed to gain a 360° view of targeted consumers and losing customers to competitors who already harness this real-time data.

When it comes to managing and operating data operations, the possibilities are as endless as… the potential risks.

Here are some examples of the business transformation possibilities associated with big data

Business intelligence

The term Business Intelligence was coined to describe the import and analysis of big data and its application in business operations. Business Intelligence (BI) is an indispensable weapon for success in increasingly competitive markets. By tracking and predicting activity and challenge points, Business Intelligence puts the company’s big data at the service of its products.


Due to its ability to analyze the myriad of interactions, patterns, and anomalies that occur in an industry and a market, big material facilitates the bringing to market new and creative products and tools. Imagine that the company “April Drinks” analyzes its big data and discovers that, during the summer months, item B has nearly twice as many sales as item A in the PACA region, while sales of these two items remain more or less equal on the west coast and in the Centre-Val de Loire. The company decides to develop a marketing tool to publish campaigns on social media and target PACA markets with advertisements highlighting the popularity and immediate availability of item B. In other words, the company leverages its big material by offering new or customized products and serving advertisements that maximize its potential profits.

Reduced TCO

Are the savings as important as the turnover? Big material is ready to show you. IT professionals measure operations not by the purchase price of equipment, but by a variety of factors, including annual contracts, licenses, and personnel overhead. Big material operations can quickly materialize knowledge in areas where resources are underutilized and in areas that require the most attention. The availability of these different types of information allows managers to plan budgets that are flexible enough to operate in a modernized environment.

In most sectors, companies and brands use big data to innovate. For example, shipping companies rely on big materials to calculate transit times and set their rates accordingly. Big material is at the origin of revolutionary scientific and medical discoveries because it makes it possible to have a capacity for analysis and study of a power never reached before. And this development also has an impact on our daily lives.

Big data – Five V +1

The future of big material is often described by five words beginning with the letter V, and each aspect must be approached individually while taking into account its interactions with the other aspects.

Volume – Develop a plan for the volumes of data to be processed, and describe how and where this data will be stored.

Variety –We will identify the different sources of data used in the ecosystem under consideration and equip ourselves with the tools that will allow us to import this data efficiently.

Speed ​​– Recall that speed is an essential aspect of successful businesses. We will identify and deploy technologies that ensure the development of the big material picture as close to real-time as possible.

Veracity – The output quality of a treatment depends closely on the quality of its input (GIGO): you must ensure that the input data is correct and clean.

Value – Not all data is equally important, so you need to create a big data environment that generates actionable business intelligence information in an easy-to-understand form.

And we do not hesitate to quote a sixth V:

Virtue – Organizations must take into account the ethical considerations of using big data, which include knowing and respecting all regulations related to compliance and confidentiality of this type of data.

The Future of big data and data lake analytics

Big data is not new material, but rather material used for new use cases and new ideas. Its analytics is the process of examining very large, granular datasets to discover buried structures, unknown correlations, new market trends, customer preferences, and new actionable insight. ‘business.

Previously, traditional material warehouses limited employees from making queries that were not possible by storing only aggregated material.

Imagine the Mona Lisa confined to coarse pixels: that’s pretty much the view you have of your customers in a material warehouse. To get a finer-grained view of your customers, you need to store fine-grained, granular, nano-level material about those customers and apply big data analytics processes such as material mining or machine learning.

A material lake is a centralized storage location that contains big data from many sources in a raw, granular format. It can store structured, semi-structured, or unstructured material, allowing for the preservation of material in more flexible formats for later use. When it imports the material, the material lake associates it with identifiers and metadata tags for faster recovery. With data lakes, material scientists can access, prepare, and analyze material faster and with greater accuracy. For analysts, this vast pool of material available in various non-traditional formats provides a single solution for accessing the information needed for different use cases such as fraud detection or customer sentiment analysis. customers, consumers, or Internet users.

How to use big data

To fully understand all of the above, you must first know the basic products of big data: these are generally Hadoop, MapReduce, and Spark (three products developed under the Apache Software Projects).

Hadoop designs open-source software solutions specifically for the future of big data mining and allows the distribution of the processing load required for processing huge datasets across a few nodes or a few hundred thousand separate processing nodes. Instead of moving a petabyte of material to a tiny processing site, Hadoop does the opposite, dramatically accelerating dataset processing speeds.

MapReduce performs two functions: compiling and organizing (Map, map) the datasets, and then reducing (Reduce) them into smaller, structured datasets ready to serve queries or internal business tasks.

Spark is also an Apache Foundation open-source project. It is a super-fast distributed framework for large-scale processing and machine learning. Spark’s processing engine can run as a stand-alone system, a cloud service, or on top of popular distributed systems (e.g., Kubernetes or its predecessor: Apache Hadoop).

These and other Apache tools are arguably the most reliable ways to leverage big data in your business.

The Rise and Future of big data

With the explosion of cloud technologies, the need to find a solution that can handle ever-increasing volumes of material has become a prime consideration for the design of digital architectures. In a world where transactions, inventory, and even IT infrastructure can exist in purely virtual form, an effective big data approach must be able to generate a holistic view by importing and processing material from a large number of sources, including:

  • Virtual network logs
  • Security Events and Patterns
  • Distribution of traffic in networks
  • Anomaly detection and resolution
  • Compliance Information
  • Tracking customer behavior and preferences
  • Geolocation material
  • Social channel material (for tracking user sentiment towards brands)
  • Inventory level and shipment tracking
  • Other specific materials that have an impact on the activities considered

Analyzes of big data trends – even the most cautious ones – parallel the continued reduction of physical on-premises infrastructure and the exponential adoption of virtualization technologies. This growing dependence on tools and partners capable of managing a new universe accompanies the evolution, wherein bits and bytes simulate the virtual replica of machines, replacing them.

Big data is not only an important aspect of the future, it can be the future itself.

The evolution of our solutions for storing, moving, and understanding data will continue to influence the approach taken by companies and their IT department.

Big data, cloud, and serverless processing

Before cloud platforms were introduced, organizations performed all management and processing on-premises. However, with the emergence of cloud platforms such as Microsoft Azure, Amazon AWS, Google Cloud, and others, enterprises have started to flock to solutions based on cloud-managed big material clusters.

This evolution has encountered many difficulties, in particular cases of inefficient use, underuse, or overuse depending on the period. To break free from the problems associated with cloud-managed clusters, the best solution is “serverless architecture”, which has the following advantages:

You will pay only for the applications you use.

Organizations used to perform all management and processing on-premises before the introduction of cloud platforms.

  • Reduced Implementation Time – Unlike deploying a managed cluster which can take hours or days, installing a serverless big data processing application takes minutes.
  • Incident tolerance and availability – By default, a server-less architecture managed by a cloud service provider guarantees levels of incident tolerance and availability specified in a service level agreement (SLA). Moreover, this type of architecture does not need administrators.
  • Auto-scaling – Pre-defined auto-scaling rules allow the application to scale in close alignment with workloads, significantly reducing processing costs.

What qualities should a big data integration tool have?

Specialized tools greatly simplify the big data integration process. Features to look for in a big data integration tool include:

  • Many connectors – There are many systems and applications in the world: the more your big data integration tool offers predefined connectors, the more your team will save time.
  • Open source – Open source architectures generally offer more flexibility while helping to avoid captive provisioning; moreover, the big data ecosystem is based on open-source technologies that it is advisable to adopt and use.
  • Portability – As enterprises increasingly adopt hybrid cloud models, it’s important to be able to build your big data integrations once and run them anywhere: on-premises, in the cloud, or in hybrid mode.
  • Ease of use – Big data integration tools should be easy to learn and use, for example with a graphical interface that makes it easy to visualize your big data pipelines.
  • Transparent pricing model – A trusted big data integration tool provider shouldn’t afford to increase your billing every time you add connectors or multiply material volumes.
  • Cloud compatibility – Your big data integration tool should be able to run natively in a single-cloud, multi-cloud, or hybrid environment. It should also be able to run in containers and use a server-less architecture to minimize the cost of processing your big data and only pay for what you use and not idle servers.
  • The business should aggregate and supervise relevant material from the outside world before releasing it to business users to avoid potential liability. Whenever you choose a tool or platform for your big data, make sure it incorporates the required material quality and material governance features.

Talend and big data

 Talend offers powerful tools for integrating and processing your big data. By using these Talend tools for big data integration, IT engineers can complete integration tasks 10 times faster than manual coding, and at a fraction of the cost of our competitors.

  • Native mode – Talend solutions run natively on cloud and big data platforms. They generate native code that can run directly in the cloud, without requiring a server or a big data platform and without the need to install and maintain proprietary software on each node and each cluster, resulting in a considerable reduction in overhead costs.
  • Open source – Talend solutions are open source and based on open standards: we integrate the most effective innovations of the cloud and big data ecosystems and we make them benefit our customers.
  • Unified – Talend offers a single platform and comprehensive portfolio for material integration (material quality, MDM, application integration, and material catalog) and material interoperability with complementary technologies.
  • The Talend Platform offers a subscription license based on the number of developers using it, not on the volume of material or the number of connectors, CPUs/cores, clusters, or nodes. Prices per number of users are more predictable and do not entail a data volume adjustment (material tax) for the use of the products.

Talend the future of big data Platform offers additional features: administration and monitoring capabilities, material quality directly integrated into the platform, and dedicated technical support on the web, by email, and by phone.

Our solution also offers multi-cloud native functionality, scalability for all project types, and 900 built-in connectors.

Talend Real-Time Big Data Platform allows you to do all of these operations, but also to take advantage of the performance of Spark Streaming in real time for your big data projects.

First steps with big data

What are you waiting for to discover the trial version of the Talend Big material Platform? Talend Big Material Platform simplifies complex integrations and leverages Spark, Hadoop, NoSQL, and the cloud to help you turn your material into actionable insight faster. Check out our Getting Started with Big Data guide to maximize your experience during the free trial.

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