The Future of Big Data

Early this century the rise of the relational database, public web access, wireless, and other technologies made the study and management of massive data sets a real and present challenge that needed a name. In July of 2013 the Oxford English Dictionary adopted the phrase, “big data,” but it’s been around since as early as World War II to apply to working with massive amounts of information.

Big data refers to data sets that are too large and complex for traditional data processing and data management applications.  Big Data became more popular with the advent of mobile and IoT technology, with people producing more and more data (geolocation, social apps, fitness apps, etc…) and accessing digital data on their devices.

It has also become the catch-all term for gathering, analyzing, and using massive amounts of digital information to improve business operations. As data sets continue to grow, and applications continue to become more real-time, big data and big data processing are more and more moving to the cloud.

Download The Forrester Wave™: Big Data Fabric, Q2 2018 now.
Download Now

Why is Big Data So Important?

Consumers live in a digital world of instant expectation. From digital sales transactions to marketing feedback and refinement, everything in today’s cloud-based business world moves fast. All these rapid transactions produce and compile data at an equally speedy rate. Putting this information to good use in real-time often means the difference between capitalizing on information for a 360 view of the target audience, or losing customers to competitors who do.

The possibilities (and potential pitfalls) of managing and utilizing data operations are endless. Here are a few of the most important ways big data can transform an organization:

  • Business intelligence - Coined to describe the ingestion, analysis, and application of big data for the benefit of an organization, business intelligence is a critical weapon in the fight for the modern market. By charting and predicting activity and challenge points, business intelligence puts an organization’s big data to work on behalf of its product.
    .
  • Innovation - By analyzing a periscope-level view of the myriad interactions, patterns, and anomalies taking place within an industry and market, big data is used to drive new, creative products and tools to market.

    Imagine “Acme Widget Company” reviews its big data picture and discovers that in warmer weather, Widget B sells at a rate of nearly double Widget A in the Midwest, while sales remain equal on the West Coast and in the South. Acme could develop a marketing tool that pushes social media campaigns that target Midwestern markets with unique advertising highlighting the popularity and instant availability of Widget B. In this way, Acme can put its big data to work with new or customized products and ads that maximize profit potential.
    .
  • Lowered cost of ownership - If a penny saved is a penny earned, then big data brings the potential to earn lots of pennies. IT professionals measure operations not by the price tags on equipment, but on a variety of factors, including annual contracts, licensing, and personnel overhead.

    The insights unearthed from big data operations can quickly crystalize where resources are being underutilized and what areas need more attention. Together this information empowers managers to keep budgets flexible enough to operate in a modern environment.

Organizations and brands in almost every industry are using big data to break new ground. Shipping companies rely on it to calculate transit times and set rates. Big data is the backbone of groundbreaking scientific and medical research, bringing the ability to analyze and study at a rate never before available. And it impacts how we live each day.

Download The Future of Big Data now.
View Now

The Five Vs of Big Data +1

Big Data is often qualified by the 5 Vs by industry experts, each of these should be addressed individually and with respect to how it interacts with the other pieces.

Volume - Develop a plan for the amount of data that will be in play, and how and where it will be housed.

Variety - Identify all the different sources of data in play in an ecosystem and acquire the right tools for ingesting it.

Velocity - Again, speed is critical in modern business. Research and deploy the right technologies to ensure the big data picture is being developed in as close to real-time as possible.

Veracity - Garbage in, garbage out, so make sure the data is accurate and clean.

Value - Not all gathered environmental information is of equal importance, so build a big data environment that surfaces actionable business intelligence in easy to understand ways.

And we’d like to add one more:

Virtue –the ethics of Big Data usage also need to be addressed in light of all the regulations for data privacy and compliance.

Big Data Analytics and Data Lakes 

Big Data is really about new use cases and new insights, not so much the data itself. Big Data Analytics is the process of examining very large, granular data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and new business insights. People can now ask questions that were not possible before with a traditional data warehouse as it could only store aggregated data.

Imagine for a minute looking at a painting of Mona Lisa and only seeing big pixels, well, this is the view you’re getting from customers in a data warehouse. In order to get the fine-grained view of your customers, you’d need to store fine, granular, nano-level data about these customers and use big data analytics like data mining or machine learning to see the fine-grained portrait.

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, which means 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. Data scientists can access, prepare, and analyze data faster and with more accuracy using data lakes. For analytics experts, this vast pool of data—available in various non-traditional formats—provides the unique opportunity to access the data for a variety of use cases like sentiment analysis or fraud detection.

How to Use Big Data 

Getting a handle on all of the above starts with the basics. In the case of big data those usually involve Hadoop, MapReduce and Spark, 3 offerings from the Apache Software Projects.

Hadoop is an open-source software solution designed for working with big data. The tools in Hadoop help distribute the processing load required to process massive data sets 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 reverse, vastly speeding the rate at which information sets can be processed.

MapReduce, as the name implies, helps performs two functions: compiling and organizing (mapping) data sets, then refining those into smaller, organized sets used to respond to tasks or queries.

Spark is also an open source project from the Apache foundation, it is an ultra-fast, distributed framework for large-scale processing and machine learning. Spark’s processing engine can operate as a stand-alone install, a cloud service, or anywhere popular distributed computing systems like Kubernetes or Spark’s predecessor, Apache Hadoop, already run.

These and other tools from Apache are among the most trusted ways of putting big data to good use in your organization.

The Rise and Future of Big Data 

With the explosion of cloud technologies, the need to wrangle an ever-growing sea of data became a ground-floor consideration for designing digital architecture. 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 overview by ingesting data from many sources, including:

  • Virtual network logs
  • Security events and patterns
  • Global network traffic patterns
  • Anomaly detection and resolution
  • Compliance information
  • Customer behavior and preference tracking
  • 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 big data trends points toward a continual reduction in on-site physical infrastructure and an increasing reliance on virtual technologies. With this evolution will come a growing dependence upon tools and partners that can handle a world where machines are being replaced by bits and bytes that emulate them.

Big data isn’t just an important part of the future, it may be the future itself. The way that business, organizations, and the IT professionals who support them approach their missions will continue to be shaped by evolutions in how we store, move and understand data.

Big Data, the Cloud, and Serverless Computing 

Before the introduction of the cloud platforms, all the big data processing and managing was done on-premises. However, with the introduction of cloud-based platforms such as Microsoft Azure, Amazon AWS, Google Cloud, etc. led to Big Data Managed Cluster to be deployed in the cloud.

This came with many difficulties such as improper utilization, underutilization, or overutilization in certain time periods. To abstract away the problems associated with Managed Cluster, the best solution is serverless architecture, which has the following benefits:

  • Only pay for the application you use – Both storage layer and computation layer are decoupled, you pay for as long as you keep the amount of data in the storage layer and for the amount of time it takes to do the needed calculation.
  • Decreased time of implementation – Unlike deploying a managed cluster which takes hours to days, the serverless big data application takes only a few minutes.
  • Fault tolerance and availability – By default, serverless architecture which is managed by a cloud service provider offers fault tolerance, availability based on a service-level agreement (SLA). So there is no need for an admin.
  • Easy scale & auto scale – Defined auto scale rules enable to scale in and scale out application according to workload. This helps to significantly reduce the cost of processing.

What Should You Look For in a Big Data Integration Tool?

Big data integration tools have the potential to simplify this process a great deal. The features you should look for in a big data tool are:

  • A lot of connectors: there are many systems and applications in the world. The more pre-built connectors your big data integration tool has, the more time your team will save.
  • Open-source: open-source architectures typically provide more flexibility while helping to avoid vendor lock-in; also, the big data ecosystem is made of open source technologies you’d want to use and adopt.
  • Portability: it's important, as companies increasingly move to 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 easy to use with a GUI interface to make visualizing your big data pipelines simpler.
  • A transparent price model: your big data integration tool provider should not ding you for increasing the number of connectors or data volumes.
  • Cloud compatibility: your big data integration tool should work natively in a single cloud, multi-cloud, or hybrid cloud environment, be able to run in containers and use serverless computing to minimize the cost of your big data processing and pay for just what you use and not idle servers.
  • Integrated data quality and data governance: big data usually comes from the outside world and the relevant data has to be curated and governed before being released to business users or else it could become a huge company liability. When choosing a big data tool or platform, make sure it has data quality and data governance built in.

Big Data with Talend

 Talend offers robust big data integration tools for integrating and processing big data. Using Talend for big data integration, data engineers can complete integration jobs 10 times faster than hand coding, at a fraction of our competitors’ cost.

  • Native: Talend runs natively on cloud and big data platforms. Talend generates native code that can run directly inside a cloud, in a serverless fashion or Big Data platform with no need to install and maintain proprietary software on each node and cluster –thus eliminating overhead costs.
  • Open: Talend is open source and open standards-based, which means that we embrace the latest innovations from the Cloud and Big Data ecosystems and our customers can as well.
  • Unified: Talend provides a single platform and an integrated portfolio for data integration (including data quality, MDM, application integration & data catalog), and interoperability with complementary technologies.
  • Pricing: Talend platform is offered via a subscription license based on the number of developers using it vs. the data volume of number of connectors, CPUs or cores, clusters or nodes. Pricing by users is more predictable and does not charge a “data tax” for using the product.

Talend Big Data Platform offers additional features like management and monitoring capabilities, data quality built right into the platform, and additional support on web, email, and phone.

It also offers native multi-cloud functionality, scalability for any kind of project, and 900 built-in connectors.

Talend Real-Time Big Data Platform allows you to do all this plus turn the real-time Spark Streaming turbo for your big data projects.

Getting Started with Big Data 

Give Talend's Big Data Platform a try today. Talend Big Data Platform simplifies complex integrations to take advantage of Spark, Hadoop, NoSQL, and cloud, so your business can get insights from data faster. And to make the most of your free trial, check out our Getting Started With Big Data guide. 

| Last Updated: November 29th, 2018