How does big data affect?

The future of big data - definition and application

The term “Big Data” was added to the Oxford English Dictionary in July 2013. But long before that, during World War II, the term was circulating as a paraphrase for working with massive data. With the advent of relational databases, the Internet, wireless and other technologies, the challenge of analyzing and managing large data sets increased. Big data came to the fore.

What is big data?

Big data refers to data sets that are too large and complex for traditional computing and data management applications. This growing amount of data came mainly with mobile and IoT technologies. Because through geolocation, social apps and the like, people are generating more and more data and accessing it digitally.

Big data is now a collective term for everything that has to do with the acquisition, analysis and use of huge amounts of digital information as well as with process optimization. As data sets are getting bigger and bigger and applications are more and more real-time capable, big data is increasingly moving to the cloud.

Why is big data so important?

In our digital world, consumers would like to have their wishes fulfilled immediately. Therefore, all online business processes run at a very fast pace, from sales transactions to marketing feedback and optimization. Data is generated and collected at the same speed. This so-called big data is extremely important for companies. They give them a 360-degree view of their target groups that they can use to their advantage.

In almost all industries, companies rely on big data to identify trends and initiate innovations. Shipping companies, for example, use it to calculate transit times and set tariffs. Big data forms the basis for groundbreaking scientific and medical research projects and enables analyzes and studies to be carried out faster than ever before. Big data also has an impact on our daily lives.

The opportunities (and potential challenges) in managing and leveraging data operations are endless. In the following you will find out in which areas big data is used and to what extent companies can benefit from it.

Business Intelligence: The Application of Big Data

Business intelligence is the process by which big data can be ingested, analyzed and applied to generate benefits for an organization. This makes it an important tool in the battle for market share. By displaying and predicting opportunities and challenges, organizations with business intelligence can use their big data optimally for their success.

Innovation with big data: example

Business processes can be innovated with the help of big data analyzes. They are used to precisely analyze the interactions, patterns and anomalies within an industry and a market - and thus bring new, creative products and tools to the market.

Example:Let's say the company Mustermann Corp. analyzes its big data. It found that in warm weather, product B sells almost twice as often in the Midwest as product A. Sales on the west coast and in the south remain the same. The Mustermann Corp. could then develop a marketing tool that would run social media campaigns for markets in the Midwest. It is intended to highlight the popularity and immediate availability of product B. In this way, the company could make optimal use of its big data to support new or customized products and advertisements. In this way it increases its profit potential.

Resource planning: lower operating costs thanks to big data

Big data has the potential to reduce costs in companies. For IT experts, operational processes and their costs are made up of a number of factors, such as annual contracts, licensing or personnel costs. With the help of big data, it is possible to determine exactly where resources are going. This is how companies immediately recognize ...

  • ... where resources are underutilized.
  • ... which areas need more attention.

With the help of big data, executives can design and control budgets better. With the help of real-time data, they can react flexibly to changes and reschedule resources in good time if necessary.

The five Vs of Big Data + one more

Industry experts often process big data with the so-called "5 Vs". Look at each of these five elements for yourself without losing sight of the interactions between them.

  • Volume - Develop a plan for the intended amount of data. Think about how and where this data should be stored.
  • Variety - Identify all the different data sources in an ecosystem and acquire the right tools for data ingestion.
  • Velocity - Research and implement the appropriate technologies so that you get a clear picture of your big data. This will bring you as close as possible to real time.
  • Veracity - Make sure your data is accurate and clean.
  • Value - Not all information collected is equally important. Create a big data environment that presents meaningful BI insights in an understandable way.

And we would like to add another V:

  • Virtue - The ethics of big data usage must not be forgotten in view of the numerous data protection and compliance regulations.

Store and analyze big data: data warehouses vs. data lakes

Big data is primarily about new use cases and new insights, not so much about the data itself. Big data analyzes are used to check very large, granular data sets for:

  • hidden patterns
  • unknown correlations
  • Market trends
  • Customer preferences
  • new, business-relevant insights

There are two popular solutions for storing big data for analysis: data warehouses and data lakes.

Traditional data warehouses

Large amounts of information can be stored in a data warehouse and made available for further analysis. In contrast to a data lake, the data warehouse brings together various data in standardized formats and structures. These depend on what kind of analysis is to be carried out. This means that a data warehouse only contains aggregated data such as key figures or transaction data. You can only save data that has already been processed and that serves a specific purpose. It is difficult to change the information stored in it.

Data lakes as a storage location for big data

Data lakes are a central storage repository that holds big data from many sources in a raw, granular format. It can store structured, semi-structured, or unstructured data. I. E. the data can be kept in a more flexible format for later use.

A data lake combines data as it is stored with identifiers and metadata tags for faster access. With data lakes, data scientists can retrieve, prepare, and analyze data faster and with greater accuracy. Analysis experts can call up data for various use cases such as sentiment analyzes or for fraud detection from this data pool as required.

How You Can Use Big Data: Basic Tools

To get support and make sense of big data, companies can use a few basic tools. This usually includes Hadoop, MapReduce, and Spark, three offerings from the Apache Software Projects. With these and other software solutions, you can use big data in your company.

Hadoop

Hadoop is an open source software solution for big data. The tools in Hadoop help distribute the process load. The massive data sets can be run on several - or hundreds of thousands - separate computing nodes. Instead of transferring a petabyte of data to a small processing location, Hadoop does the opposite. In this way, it ensures that information is processed much faster.

MapReduce

MapReduce supports the execution of two functions:

  1. Compiling and organizing (mapping) data sets.
  2. The subsequent refinement into smaller, organized data sets to respond to tasks or queries.

Spark

Spark is also an open source project from the Apache Foundation. It's an ultra-fast, distributed framework for big data processing and machine learning. Spark's processing engine can be used in the following ways:

  • as a stand-alone installation
  • as a cloud service
  • in all common distributed IT systems such as Kubernetes or Sparks ‘predecessor, Apache Hadoop

Big data sources

Cloud technologies are constantly evolving and leading to ever larger floods of data. In order to be able to build future-oriented digital solutions, this information must be processed. For virtual transactions, inventories and IT infrastructures, a well thought-out big data approach with data from numerous sources is therefore required. Only in this way does big data enable a holistic overview. The following sources can be used:

  • Virtual network protocols
  • Security-related events and patterns
  • Global network traffic patterns
  • Detection of anomalies and resolution
  • Compliance information
  • Customer behavior and preference tracking
  • Geolocation data
  • Social channel data for brand sentiment tracking
  • Inventory and shipment tracking
  • Other specific data that is important to your organization.

The future of big data

Even careful big data trend analyzes assume a continuous reduction in local, physical infrastructures and an increase in virtual technologies. This creates a growing dependency on various tools and partners.

The use of big data will increase rather than decrease in the future. The way in which companies, organizations and their IT experts solve tasks is based on the developments in data and IT technology. This means that there will always be new solutions with which big data can be stored, analyzed and processed.

Big data, the cloud and serverless computing

Before there were cloud platforms, companies processed and managed all business data locally. It was only with the advent of Microsoft Azure, Amazon AWS, or Google Cloud that organizations started using Big Data Managed Clusters.

However, this created new challenges. For example, people used big data managed clusters inappropriately or too often or too seldom in certain periods of time. A serverless architecture is a great way to get problems with managed clusters under control and to benefit from the following advantages:

  • Low cost: You only pay as long as your data is on the storage tier and the necessary processing continues. The storage and computation levels are separated from each other.
  • Shorter implementation time: In contrast to the implementation of a managed cluster, the serverless big data application only takes a few minutes.
  • Fault tolerance and availability: Serverless architectures managed by a cloud service provider offer fault tolerance and availability based on a service level agreement (SLA) by default. An administrator is not necessary.
  • Simple (auto) scaling: Thanks to defined auto-scaling rules, the capacities for your application can be increased or reduced depending on the workload. This can significantly reduce your processing costs.

What should you look for in a big data integration tool?

Big data integration tools can greatly simplify integration processes. Your tool should ideally offer the following features:

  • Many connectors: There are many different systems and applications around the world. The more ready-made connectors your big data integration tool has, the more time your team will save.
  • Open Source: Open source architectures usually offer more flexibility and usually do not tie you to one provider; in addition, the big data ecosystem consists of open source technologies.
  • Portability: This enables companies to rely on hybrid cloud models. You create big data integrations only once and then run them from anywhere - in on-premises, hybrid or cloud-based environments.
  • User friendliness: Big data integration tools should be easy to use and have a graphical user interface that makes it easy to visualize your big data pipelines.
  • A transparent pricing model: Under no circumstances should your provider charge a surcharge if you increase the number of your connectors or the data volume.
  • Cloud Compatibility: Your big data integration tool should work natively in a single cloud, multi cloud, or hybrid cloud environment. Ideally, it runs in containers and can also use serverless computing. This minimizes the costs of your big data processing, so that you only pay for the resources you use.
  • Integrated data quality and data governance: Big data datasets mostly come from external sources. For security reasons, they should be curated by built-in data quality and data governance functions before business users start using them.

Big data with Talend

Talend offers robust tools for integrating and processing big data. With our solutions, data engineers can complete integration jobs ten times faster than hand coding - and at a fraction of the cost of our competitors.

  • Native: Talend generates native code that can run directly in a cloud, serverless or on a big data platform. This eliminates the need to install and maintain proprietary software on every node and cluster - and significantly reduce your administrative costs.
  • Open: Talend is based on open source technologies and open standards. This means that we use the latest innovations from the cloud and big data ecosystems and let our customers benefit from them.
  • Uniformly: Talend offers a central platform and an integrated portfolio for data integration (with data quality, MDM, application integration and data catalog) as well as interoperability with complementary technologies.
  • Price: The Talend platform is provided through a subscription license. This is based on the number of developers working on the platform - not on the volume of data, the number of connectors, CPUs, cores, clusters or nodes. The costs per user can be planned and do not include any “data tax” to use the product

Other practical functions of the Talend Big Data Platform

With the Talend Big Data Platform you can look forward to additional features such as:

  • Management and monitoring functions
  • Data quality integrated directly in the platform
  • additional support on the web as well as via email and telephone
  • native multi-cloud functionality
  • Scalability for projects of all kinds
  • 900 integrated connectors

With the Talend Real-Time Big Data Platform you also benefit from turbo-fast real-time Spark streaming for your big data projects.

Get started with big data

Try the Talend Big Data Platform today. It simplifies complex integrations so that your company can use Spark, Hadoop, NoSQL and the cloud efficiently and draw insights from their data more quickly. Our “Getting Started with Big Data” guide explains how to get the most out of your free trial.