illustration de big data

Definition: Big Data

The term “Big Data” began appearing in dictionaries over the past decade, but the concept itself has been around at least since World War II. More recently, wireless connectivity, Internet 2.0, and other technologies have made managing and analyzing large data sets a reality for all of us.

By “Big Data”, we mean data sets too large and complex for traditional processing applications and data management. Big data became more popular with the advent of mobile technology and the Internet of Things as people produced more and more data with their devices. Consider, for example, data generated by location-based services, web browsing histories, social media activity, or even fitness apps.

The term can also refer to the processes of collecting and analyzing massive amounts of digital information to produce business intelligence. As data sets continue to grow and applications produce more data in real-time and continuously, businesses are turning to the cloud to store, manage and analyze their big data.

What makes Big Data so important?

Consumers live in a digital world where waiting is instantaneous. From digital sales transactions to feedback and marketing refinements, everything is evolving rapidly in today’s cloud-based business world. All of these rapid transactions produce and compile data at an equally rapid rate. Leveraging this information in real time is often the difference between capitalizing on the information for a 360-degree view of the target audience, or losing customers to competitors who do so.

The possibilities (and potential pitfalls) of managing and using data operations are endless. Here are some of the key ways big data can transform an organization:

Economic intelligence : Designed to describe the ingestion, analysis and application of important data for the benefit of an organization, business intelligence is an essential weapon in the fight for the modern marketplace. By mapping and predicting activity and challenge points, business intelligence puts an organization’s big data to work for its product…

Innovation : By analyzing a periscope-level view of the myriad interactions, patterns, and anomalies that occur within an industry and market, big data is used to bring products and new and creative tools.

Imagine that company ‘X’ reviews its Big Data and discovers that in hot weather, product B sells at a rate close to double that of product A in the south of France, while sales remain constant in the north or to the east of France. Company ‘X’ could develop a marketing tool that pushes social media campaigns that target the South of France markets with a unique advertisement highlighting the popularity and instant availability of Product B. In this way, company ‘ X’ can use its Big Data to drive new or personalized products and advertisements that maximize profit potential.

Reduced cost of ownership : If a penny saved is a penny earned, then big data saves a lot of pennies. IT professionals measure operations not by the price of equipment, but by a variety of factors, including annual contracts, licensing, and overhead staff costs.

Insights gained from big data can quickly help determine where resources are underutilized and what areas need greater attention. Together, this information allows managers to maintain budgets flexible enough to operate in a modern environment.

In almost all sectors, organizations and brands use big data to innovate. Shipping companies use it to calculate transit times and set rates. Big data is the backbone of groundbreaking scientific and medical research, enabling analysis and study at a pace never before possible. And they have an impact on our daily lifestyle.

Analytics, Data Centers and Data lakes

Big Data is actually about new use cases and ideas, not so much about the data itself. Big data analytics involves examining very large, granular data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and new business ideas. People can now ask questions that weren’t possible before with a traditional data warehouse because it could only store aggregated data.

Imagine for a moment that you are looking at a painting of the Mona Lisa and all you see are large pixels. This is the view you have of customers in a data center. In order to get a fine-grained view of your customers, you need to store fine, granular, nano-level data about those customers and use big data analytics like data mining or machine learning to see the fine-grained picture.

Data lakes are a central storage repository that contains important data from numerous sources in a raw, granular format. It can store structured, semi-structured or unstructured data, which means the data can be kept in a more flexible format for future use. When storing data, a data lake associates it with identifiers and metadata tags for faster retrieval. Scientists can access, prepare and analyze data faster and more accurately using data lakes. For analytics experts, this vast reservoir of data – available in various non-traditional formats – provides a unique opportunity to access data for various use cases, such as sentiment analysis or fraud detection.

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Common tools for unusual data

To understand all of the above, you have to start with the basics. In the case of Big Data, these are usually Hadoop, MapReduce and Spark, three offerings from the Apache Software project.

Hadoop is an open source software solution designed for working with Big Data. Hadoop tools make it possible to distribute the processing load needed to process sets of Big Data across a few or a few hundred thousand separate computing nodes. Instead of moving a petabyte of data to a tiny processing site, Hadoop does the opposite, dramatically accelerating the speed at which sets of information can be processed.

MapReduce, as the name suggests, helps perform two functions: compiling and organizing (mapping) data sets, then refining them into smaller, organized sets used to answer tasks or queries.

Spark is also an open source project of the Apache Foundation, it is a lightning-fast distributed framework for large-scale processing and machine learning. Spark’s processing engine can run as a standalone installation, a cloud computing service, or anywhere popular distributed computing systems like Kubernetes or Spark’s predecessor, Apache Hadoop, already run.

These and other tools from Apache are some of the most reliable ways to put big data to use in your organization.

Future uses of Big Data

With the explosion of cloud computing technologies, the need to contend with ever-increasing amounts of data has become a primary consideration for digital architecture design. In a world where transactions, inventory, and even IT infrastructure can exist in a purely virtual state, a good big data approach creates a holistic view by ingesting data from many sources, including:

  • Virtual network logs
  • Security events and patterns
  • Global network traffic patterns
  • Anomaly detection and resolution
  • Compliance Information
  • Tracking customer behavior and preferences
  • Geolocation data
  • Social Channel Data for Brand Sentiment Tracking
  • Inventory levels and shipment tracking
  • Other specific data that impacts your organization

Even the most conservative analysis of mega data trends indicates a continued reduction in on-premises physical infrastructure and an increasing reliance on virtual technologies. This evolution will be accompanied by a growing dependence on tools and partners capable of managing a world where machines are replaced by bits and bytes that emulate them.

Big data is not only an important part of the future, it can be the future itself. How businesses, organizations, and the IT professionals that support them approach their missions will continue to be shaped by the evolving way we store, move, and understand data.

Big Data, the Cloud and Serverless Computing

Before the introduction of cloud platforms, all big data processing and management was done on-premises. The introduction of cloud-based platforms such as Microsoft Azure, Amazon AWS and Google BigQuery now makes it beneficial (and advantageous) to carry out data management processes remotely.

Cloud computing on a serverless architecture offers a series of benefits to businesses and organizations, including:

Efficiency – Both the storage layer and the compute layer are decoupled, you pay for as long as you keep the amount of data in the storage layer and for the time it takes to do the necessary computation.

Reduction in implementation time – Unlike deploying a managed cluster which takes hours or even days, applying serverless big data only takes minutes.

Fault tolerance and availability – By default, serverless architecture that is managed by a cloud service provider offers fault tolerance, availability based on a service level agreement (SLA). It is therefore not necessary to call on an administrator.

Ease of scaling and auto-scaling – Defined auto-scaling rules allow the application to scale according to workload. This significantly reduces the cost of treatment.

Choosing a tool for Big Data

Great data integration tools can significantly simplify this process. The features you should look for in a tool for big data management are:

Lots of connectors : There are many systems and applications in the world. The more pre-built connectors your large data integration tool has, the more time your team will save.

Open-source : open-source architectures generally offer more flexibility while avoiding vendor lock-in; Moreover, the big data ecosystem is made up of open-source technologies that you would want to use and adopt.

Portability : It’s important, as businesses increasingly adopt hybrid cloud models, to be able to build your big data integrations once and run them anywhere: on-premises, hybrid and in the cloud.

Ease of use : Big data integration tools should be easy to learn and use with a graphical interface to make it simple to visualize your big data pipelines.

Price transparency : Your data integration tool vendor should not blame you for increasing the number of connectors or data volumes.

Cloud Compatibility : Your data integration tool should run natively in a single cloud, multi-cloud, or hybrid environment, be able to run in containers, and use serverless computing to minimize the cost of your big data processing and pay only what you use and not idle servers.

Integrated data quality and governance : Big data usually comes from the outside world and relevant data needs to be curated and managed before being released to business users, otherwise it could become a huge liability for the business. When choosing a big data tool or platform, make sure it incorporates data quality and governance.