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Most large companies have one or more big data applications, which provide fast access to large stores of customer and sales data. As the IT organization grows new job categories and new tasks are added to the mix. These include big data hardware and software support, business analysts who use analytics to probe and explore the data, and managers who must supervise and prioritize job tasks.
As the holiday season approaches, your organization can expect more BI activity as you take advantage of a significant increase in customer interactions. The smart IT organization should be proactive and prepare now for larger data volumes and more analytics activity by tuning their big data application for high performance.
In many cases, the success of a big data application can be traced to how well it is integrated into your enterprise data warehouse. This article presents several ways to get this done quickly and efficiently from the beginning.
Today, everyone realizes that in order to reach their full performance potential, Big Data applications require some tuning. The tuning isn't easy, it's not free, and responsibility for understanding requirements and implementing the appropriate tuning methodology falls squarely on the shoulders of the database administrator. Read on to learn more.
Big data applications are not usually considered mission-critical: while they support sales and marketing decisions, they do not significantly affect core operations such as customer accounts, orders, inventory, and shipping. Why, then, are major IT organizations moving quickly to incorporating big data in their disaster recovery plans?
As big data application success stories (and failures) have appeared in the news and technical publications, several myths have emerged about big data. This article explores a few of the more significant myths, and how they may negatively affect your own big data implementation.
Most large organizations have implemented one or more big data applications. As more data accumulates internal users and analysts execute more reports and forecasts, which leads to additional queries and analysis, and more reporting. The cycle continues: data growth leads to better analysis, which generates more reporting. Eventually the big data application swells with so much data and querying that performance suffers. How to avoid this?
Big data is everywhere, and most large IT enterprises have installed one or more big data applications. These applications provide fast access to large stores of data, usually customer or sales data. Your technical staff that supports these applications and the systems that analyze and consume the data didn't exist ten years ago. Who are these new IT professionals, and how should you manage them?
Your big data repository won't simply add another twelve months of data over the next year. More data is coming, more categories of data will be created, and your analytical environment must expand to fit future needs. But size alone won't be your only problem. In the rush to accumulate a sufficient amount of valuable data and implement a business analytics environment that can produce usable results, several items may have been ignored, postponed, or simply forgotten. These missing details can make or break your company in the future.
Big data software, hardware, application suites, business analytics solutions ... suddenly, it seems, IT enterprises are deluged with vendor offerings that solve problems it didn't know it had. As you dive into what will most likely be your largest IT project of the year, ensure that you have planned and budgeted for the following items that are unique to big data implementations.
It’s difficult to simply 'drop' big data applications into an existing IT infrastructure and expect to run smoothly. In addition to energy and cooling requirements for new hardware to support the new big data application, other IT areas need to prepare. The major factors that determine whether enhancements will be needed to existing applications include large data storage needs, larger data transmission capacity, and the demands these will place on existing hardware and software.
Big Data implementations are more than just lots of data. Of equal importance is the analytics software used to query the data. Analyzing business data using advanced analytics is common, especially in companies that already have an enterprise data warehouse. It is therefore only natural that your big data application must be integrated with the existing warehouse.