Overview of Big Data Analytics Systems for an Organization

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It’s not how it used to be, people get news on their phones now, no one waits for the newspaper the next day, though people still read it. Once a newspaper was a source of fresh news is something that provides old news. All lot has changed with improvements in technology. That has changed for organisations too. Organisations are now content driven they cannot wait a day or two or the information which some companies are getting every few minutes. This will not only put them behind in the market race but may become a reason for a hefty loses in terms of money.

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This age of organisational working is mostly dependent on how fast and accurately they are able to evaluate the all so increasing data that passes through the organisations data base. There is a irregularity in data, but is the work of a big data analytics professionals to find out a way to fight through the intuitiveness of this erratic data. One can add more capacity, hardware and personnel but that might be a bad method of overcoming a problem – at the end all it might add is a lot more cost and not what it was supposed to do – translating it into better and faster awareness. Here a five big data analytics that can help an organization with the increasing pace of the technology, the size, velocity and locations side of data strategically.

GPUs to Handle Big Data Volume and Velocity

Big data analytics is pouring off real-time sources like sensors and devices, (cell phones, telematics data from cars, social media streams, server logs, and clickstreams). Much of this data requires immediate analysis, for valuable insights while the information is still relevant. Utility companies, for example, are gathering real-time insights from smart meters, to continuously balance the grid, prevent service interruptions, and reduce emissions. Legacy systems, leveraging CPU-only architectures with low parallelism, struggle to keep pace with the volume and velocity of data. As a result, they force IT to keep adding more hardware, and hiring more data engineers to pre-aggregate or index the data, just to fit it in mainstream tools. This slows down the whole data pipeline, and limits the type and amount of insights available from the data. Graphics processing units (GPUs), in combination with traditional CPU architectures, are now accelerating a new breed of high-performance database engines and visual analytics systems. These GPU-based solutions enable massive parallel processing, and can complete a query in milliseconds that would take hours on a legacy platform.

Operational Agility

In today’s high-speed world, most analysts do not have the familiar luxury of getting up to have a cup of coffee while they wait for their query to finish or their dashboard to refresh. While that query-then-wait experience is just plain frustrating, it has real value impacts too: when the analytics experience is too slow, users explore less and find fewer insights. The trend is toward providing users with an extreme analytics experience, one with zero discernible latency when interacting with the data. Users start to love doing analytics again, and the organization benefits by moving toward a continuous analysis mode, versus one with daily, weekly or monthly analysis cycles.

Shifting Roles for Unified Understanding

BI initiatives will expand as more people within the organization discover the importance of the data and find strategic and competitive value in its application. As technology improves, the value of the data is extending beyond the IT department, as more teams within the organization seek a unified understanding of the most important operational challenges. With better access to intuitive self-service platforms, job roles are shifting too (see point 5). The analyst role, for example, which was focused on the data warehouse and BI, is blurring with the data scientist role, which was traditionally focused on statistics and machine-learning. As roles and use cases evolve and expand, end-to-end platforms improve the efficiency of marshalling and moving data between different siloed solutions. An integrated platform provides higher performance and is often easier to use, as it typically works out of the box, with integration already baked into the product.

The Increasing Importance of Location Insights

Sensors and devices in mobile objects, such as phones, cars, trucks, shipping RFID tags, social media, and satellites capture and transmit data on many important processes, and most of that data today is enriched with location and a time attributes. Being able to visualize data within the context of time and location, and then display it on interactive maps, makes it easier to recognize patterns, understand complex historical relationships, and anticipate future events. Unlike traditional Geographic Information Systems (GIS), most location-based geoanalytics are lightweight, involving simple geospatial filtering and joins, and visualization with cross-filtering between traditional BI visual elements like bar, line and pie charts. Geospatial analytics is difficult at scale, because they occur in two or three dimensions and are compute intensive. Visualizing geospatial data with granular detail can overwhelm both analytics servers and the network connections between server and client. Traditional GIS platforms can’t handle the mainstream analytics components, and traditional BI platforms can’t handle the lightweight geospatial analytics needed; neither is fast enough to be interactive at scale. Back to point No. 1: GPUs change all that. They provide the computational horsepower required to do complex geospatial analytics queries, alongside traditional analytic SQL. At the same time, GPUs offer the rendering capabilities needed to visualize granular and large-scale geospatial data and to make the experience interactive.

Demand for Self-Service Analytics Platforms

As data becomes more available and analytic literacy more pervasive, organizations are focusing more on self-service analytics platforms that enable users to access the data independently. New ways for business users to ask and answer their own questions with data, and then share their insights through visualizations, are changing the way that teams work together. The second wave of BI vendors claimed that they had ushered in a new era of “self-service analytics,” in which end users could finally be free of relying on IT to prepare and analyze their data. They offered users the ability to build their dashboards, and in certain limited cases even prepare their own small datasets. However, very large datasets, stored in the data warehouse, still required a ticket to the IT team, which could take days or weeks to fulfill. New platforms, particularly those that bridge the previously separate domains of data warehousing, GIS and BI, allow users to more freely manage, query and visualize the data. High-performance GPU-based systems eliminate the need for indexing, pre-aggregation, and down sampling of the data, making data loading and preparation a far simple and faster process.

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