Big data technology landscape


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The Big data landscape emerged as one of the most powerful next-generation data management, analytics and storage.

Last year, 2015, was a year of considerable change in the ‘Art of the Possible’ with big data an analytics. Norms on big data and cultural change evolve, and as they code, leads to wholesale business, transformation and cultural change at some work environments. The change is driven not just by a fast-moving technology, but by new techniques to acquire value from data as well.

The Big data paradigm surfaced as one of the most powerful system, in terms of next-generation data management, storage and analytics. IT powerhouses actually have embraced the change and accepted that it is here to stay. What came as only a Hadoop, a distributed and storage processing platform, has truly graduated and evolved. These days, there is a whole panorama of different technologies and tools that specialize in different specific verticals of the Big Data scenario.

For all sizes of organizations, data management has changed from a vital competency to a critical differentiator that could determine market winners as well as has-beens. Fortune 100 companies and government bodies are beginning to take benefit from web pioneers’ innovations. The organizations define new initiatives and are re-evaluating current strategies to check out how they could change the business with Big Data. In the process, they learn that Big data is not one technology, initiative or technique. Instead, it is a trend across a lot of areas of technology and business.

Big data means initiatives and technologies that involve data that’s too diverse, massive or fast-changing for conventional technologies, infra-structure and skills to efficiently address. The new technologies today make it possible to realize value from Big Dat. For instance, retailers could track could keep tabs of user web clicks to determine behavioral trends that boost pricing, campaigns and stocks. Utilities could capture household usage levels of energy to predict outage and incent more energy-efficient consumption. Big Data describes sets of data so big and complex that they are impractical to manage using traditional software tools.

In particular, Big Data relates to data storage, creation, retrieval and analysis that’s remarkable in terms of velocity, volume and variety.

VELOCITY. Ad impression and clickstreams capture user behavior at millions of events every second. High-frequency stock trading algorithms reflect changes in the market within microseconds. Machine-to-machine processes exchange data between millions of devices and infrastructure and sensors generate massive real-time data log.

VOLUME. A typical personal computer had 10 gigabytes storage back in 2000. Nowadays, FB ingests 500 terabytes of new data daily. A Boeing 737 generates 240- terabytes flight data on a single flight across the United States. Smart phones proliferation, the data they make and consume, sensors embedded to daily objects soon would result in billions of constantly updated, new data feeds that contain location, environmental and other information like video.

VARIETY. Big data is not just numbers, strings and dates. Big Data also is a geospatial data, audio and video, 3D data and unstructured texting, that include social media and log files. Traditional database systems were designed to address smaller structured data volumes, lesser updates or a consistent, predictable data structure. Moreover, traditional database systems are designed to operate on one server, making increased capacity finite and expensive. Big Data databases solve the problems as well as provide organizations with the means of making tremendous business value.

With Big Data databases, enterprises could grow their revenue, save money and achieve a lot of business objectives in any vertical. Big data may also allow a company to collect billions of data points in real-time on its resources, products or customer and repackage data instantaneously to optimize the customer experience or the use of resource.

Big data enhances effectiveness and minimize the cost of current apps. Technologies could replace expensive, extremely-customized legacy systems with a standard solution, which runs on commodity hardware. And since a lot of big data technologies are open source, they could be implemented much more cheaply compared to proprietary technologies. It could help businesses to act more nimbly, enabling them to change rapidly than their competition.

1. COST REDUCTION. Big Data analytics helps businesses harness data and use it to determine new opportunities. In turn, that leads to smart business moves, higher profits, operations that are more efficient and happier customers. Big Data technologies, like Hadoop and cloud-based analytics bring considerable cost benefits in terms of storing big data amounts, and they could identify more efficient methods of doing business.
2. BETTER, FASTER DECISION MAKING. With Hadoop speed and in-memory analytics, combined with the ability of analyzing new data sources, businesses could analyze information right away and make decisions that are based on what they have learned.
3. NEW SERVICES AND PRODUCTS. With the ability of gauging customer requirements and satisfaction via analytics, comes the power of giving customers what they want. With big data analytics, more organizations are building new products to meet the needs of the clientele.

More and more organizations have successfully analyzed big data to achieve faster and better decisions, minimize costs and even new offerings for customers.

Ritesh Mehta
Ritesh Mehta works as a senior Technical Account Manager in a software development company named TatvaSoft Australia based in Melbourne. He specializes in Agile Scrum methodology, Marketing Ops (MRM) application development, Android app development, SAAS & SOA application development, Offshore & Vendor team management. Also, he is knowledgeable and well-experienced in conducting business analysis, product development, team management and client relationship management.


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