Big Data Technologies’ future is clear and unshakeable. If you have noticed, technologies like IoT, Machine Learning, artificial intelligence, and more are making their ways into our everyday lives. Behind all of these is Big Data sitting strong in an authoritative position.
Big Data Technologies? What is it?
There are devices that are talking to each other over a connected network sharing and generating data you feed, and there are algorithms learning patterns and processing information from the generated data. A simple example of the Internet of Things is your smart television that is connected to your home network and generating data on your viewing patterns, interests, and more.
In addition to spurring interest in streaming analytics, the IoT trend is also generating interest in edge computing. In some ways, edge computing is the opposite of cloud computing. Instead of transmitting data to a centralized server for analysis, edge computing systems analyze data very close to where it was created — at the edge of the network.
The advantage of an edge-computing system is that it reduces the amount of information that must be transmitted over the network, thus reducing network traffic and related costs. It also decreases demands on data centers or cloud computing facilities, freeing up capacity for other workloads and eliminating a potential single point of failure.
While the market for edge computing, and more specifically for edge computing analytics, is still developing, some analysts and venture capitalists have begun calling the technology the “next big thing.”
As organizations have become more familiar with the capabilities of big data analytics solutions, they have begun demanding faster and faster access to insights. For these enterprises, streaming analytics with the ability to analyze data as it is being created is something of a holy grail. They are looking for solutions that can accept input from multiple disparate sources, process it, and return insights immediately — or as close to it as possible. This is particularly desirable when it comes to new IoT deployments, which are helping to drive the interest in streaming big data analytics.
Several vendors offer products that promise streaming analytics capabilities. They include IBM, Software AG, SAP, TIBCO, Oracle, DataTorrent, SQLstream, Cisco, Informatica, and others. MarketsandMarkets believes the streaming analytics solutions brought in $3.08 billion in revenue in 2016, which could increase to $13.70 billion by 2021.Prescriptive Analytics.
Artificial intelligence will change data analytics by learning (using results from past analytic tasks) so that over time, results will come faster with much more accuracy. With enormous amounts of data to pull from, along with analytic results from past queries, artificial intelligence will be able to provide accurate future predictions based on current events, and one could argue that weather prediction models are a form of artificial intelligence. Further, machine learning will power business analytics by identifying potential problems or issues that might not be detected by humans. Organizations that do not deploy artificial intelligence for data analytics will fall behind competitors that use artificial intelligence for data analytics.
The adoption of in-memory computing is increasing as businesses seek quick and easy access to data and analytics to inform many business decisions. Using in-memory computing offers the insights they need to increase efficiency in operations, finances, marketing, and sales.
As improvements in in-memory computing occur, it is becoming more affordable and easier to implement, making widespread adoption inevitable in the future.
According to a Gartner report, the In-Memory Computing (IMC) market will touch around a $15 billion mark by 2021, a significant increase from $6.8 billion.
Advances in data discovery, data catalogs, data virtualization, controlled replication, integration, all aspects of governance, pervasive security, AI tools and runtimes, cheap storage and compute as well as open-source can all help to deliver on the concept and vision of the data lake.
However, assembling all the pieces that are necessary for the next generation of the data lake is non-trivial. It can be time-consuming and therefore risky and expensive. IBM Cloud Pak for Data is a Data and AI platform, microservices offering, that pre-integrates many of the capabilities needed to help deliver data lake projects that support many forms of structured and unstructured data and its processing.
As a favorite with forward-looking analysts and venture capitalists, blockchain is the distributed database technology that underlies Bitcoin digital currency. The unique feature of a blockchain database is that once data has been written, it cannot be deleted or changed after the fact. Besides, it is highly secure, which makes it an excellent choice for big data applications in sensitive industries like banking, insurance, health care, retail, and others.
Blockchain technology is still in its infancy and use cases are still developing. However, several vendors, including IBM, AWS, Microsoft, and multiple startups, have rolled out experimental or introductory solutions built on blockchain technology.
Database administrators to query, manipulate, and manage the structured data stored in relational database management systems (RDMSes).
On the other hand, NoSQL databases store unstructured data and providing fast performance. This means that it offers flexibility while handling a wide variety of datatypes at large volumes. Some examples of NoSQL databases include MongoDB, Redis, and Cassandra.