Big Data Articles

Article / Updated 12-01-2023

Getting the most out of your unstructured data is an essential task for any organization these days, especially when considering the disparate storage systems, applications, and user locations. So, it’s not an accident that data orchestration is the term that brings everything together. Bringing all your data together shares similarities with conducting an orchestra. Instead of combining the violin, oboe, and cello, this brand of orchestration combines distributed data types from different places, platforms, and locations working as a cohesive entity presented to applications or users anywhere. That’s because historically, accessing high-performance data outside of your computer network was inefficient. Because the storage infrastructure existed in a silo, systems like HPC Parallel (which lets users store and access shared data across multiple networked storage nodes), Enterprise NAS (which allows large-scale storage and access to other networks), and Global Namespace (virtually simplifies network file systems) were limited when it came to sharing. Because each operated independently, the data within each system was siloed making it a problem collaborating with data sets over multiple locations. Collaboration was possible, but too often you lost the ability to have high performance. This Boolean logic decreased potential because having an IT architecture that supported both high performance and collaboration with data sets from different storage silos typically became an either/or decision: You were forced to choose one but never both. What is data orchestration? Data orchestration is the automated process of taking siloed data from multiple data storage systems and locations, combining and organizing it into a single namespace. Then a high-performance file system can place data in the edge service, data center, or cloud service most optimal for the workload. The recent rise of data analytic applications and artificial intelligence (AI) capabilities has accelerated the use of data across different locations and even different organizations. In the next data cycle, organizations will need both high-performance and agility with their data to compete and thrive in a competitive environment. That means data no longer has a 1:1 relationship with the applications and compute environment that generated it. It needs to be used, analyzed, and repurposed with different AI models and alternate workloads, and across a remote, collaborative environment. Hammerspace’s technology makes data available to different foundational models, remote applications, decentralized compute clusters, and remote workers to automate and streamline data-driven development programs, data insights, and business decision making. This capability enables a unified, fast, and efficient global data environment for the entire workflow — from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. Control of enterprise data services for governance, security, data protection, and compliance can now be implemented globally at a file-granular level across all storage types and locations. Applications and AI models can access data stored in remote locations while using automated orchestration tools to provide high-performance local access when needed for processing. Organizations can grow their talent pools with access to team members no matter where they reside. Decentralizing the data center Data collection has become more prominent, and the traditional system of centralized data management has limitations. Issues of centralized data storage can limit the amount of data available to applications. Then, there are the high infrastructure costs when multiple applications are needed to manage and move data, multiple copies of data are retained in different storage systems, and more headcount is needed to manage the complex, disconnected infrastructure environment. Such setbacks suggest that the data center is no longer the center of data and storage system constraints should no longer define data architectures. Hammerspace specializes in decentralized environments, where data may need to span two or more sites and possibly one or more cloud providers and regions, and/or where a remote workforce needs to collaborate in real time. It enables a global data environment by providing a unified, parallel global file system. Enabling a global data environment Hammerspace completely revolutionizes previously held notions of how unstructured data architectures should be designed, delivering the performance needed across distributed environments to Free workloads from data silos. Eliminate copy proliferation. Provide direct data access through local metadata to applications and users, no matter where the data is stored. This technology allows organizations to take full advantage of the performance capabilities of any server, storage system, and network anywhere in the world. This capability enables a unified, fast, and efficient global data environment for the entire workflow, from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. The days of enterprises struggling with a siloed, distributed, and inefficient data environment are over. It’s time to start expecting more from data architectures with automated data orchestration. Find out how by downloading Unstructured Data Orchestration For Dummies, Hammerspace Special Edition, here.

Cheat Sheet / Updated 04-12-2022

Big data makes big headlines, but it’s much more than just a buzz phrase or the latest business fad. The phenomenon is very real and it’s producing concrete benefits in so many different areas – particularly in business. Here you will get to the heart of big data as a business owner or manager: You will take a look at the key terminology you need to understand the crucial big data skills for businesses, ten steps to using big data to make better decisions, and tips for communicating insights from data to your colleagues.

Cheat Sheet / Updated 03-10-2022

Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.

Cheat Sheet / Updated 02-09-2022

To stay competitive today, companies must find practical ways to deal with big data — that is, to learn new ways to capture and analyze growing amounts of information about customers, products, and services. Data is becoming increasingly complex in structured and unstructured ways. New sources of data come from machines, such as sensors; social business sites; and website interaction, such as click-stream data. Meeting these changing business requirements demands that the right information be available at the right time.

Article / Updated 03-26-2016

While the worlds of big data and the traditional data warehouse will intersect, they are unlikely to merge anytime soon. Think of a data warehouse as a system of record for business intelligence, much like a customer relationship management (CRM) or accounting system. These systems are highly structured and optimized for specific purposes. In addition, these systems of record tend to be highly centralized. The diagram shows a typical approach to data flows with warehouses and marts: Organizations will inevitably continue to use data warehouses to manage the type of structured and operational data that characterizes systems of record. These data warehouses will still provide business analysts with the ability to analyze key data, trends, and so on. However, the advent of big data is both challenging the role of the data warehouse and providing a complementary approach. Think of the relationship between the data warehouse and big data as merging to become a hybrid structure. In this hybrid model, the highly structured optimized operational data remains in the tightly controlled data warehouse, while the data that is highly distributed and subject to change in real time is controlled by a Hadoop-based (or similar NoSQL) infrastructure. It's inevitable that operational and structured data will have to interact in the world of big data, where the information sources have not (necessarily) been cleansed or profiled. Increasingly, organizations are understanding that they have a business requirement to be able to combine traditional data warehouses with their historical business data sources with less structured and vetted big data sources. A hybrid approach supporting traditional and big data sources can help to accomplish these business goals.

Article / Updated 03-26-2016

Four stages are part of the planning process that applies to big data. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode. Therefore, consider another part of your planning process and add three more stages to your data cycle. Stage 1: Planning with data: The only way to make sure that business leaders are taking a balanced perspective on all the elements of the business is to have a clear understanding of how data sources are related. The business needs a road map for determining what data is needed to plan for new strategies and new directions. Stage 2: Doing the analysis: Executing on big data analysis requires learning a set of new tools and new skills. Many organizations will need to hire some big data scientists who can understand how to take this massive amount of disparate data and begin to understand how all the data elements relate in the context of the business problem or opportunity. Stage 3: Checking the results: Make sure you aren’t relying on data sources that will take you in the wrong direction. Many companies will use third-party data sources and may not take the time to vet the quality of the data, but you have to make sure that you are on a strong foundation. Stage 4: Acting on the plan: Each time a business initiates a new strategy, it is critical to constantly create a big data business evaluation cycle. This approach of acting based on results of big data analytics and then testing the results of executing business strategy is the key to success. Stage 5: Monitoring in real time: Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to adjust or cancel a trial to avoid a lawsuit. Stage 6: Adjusting the impact: When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality. Stage 7: Enabling experimentation: Combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data. The greatest challenge for the business is to be able to look into the future and anticipate what might change and why. Companies want to be able to make informed decisions in a faster and more efficient manner. The business wants to apply that knowledge to take action that can change business outcomes. Leaders also need to understand the nuances of the business impacts that are across product lines and their partner ecosystem. The best businesses take a holistic approach to data.

Article / Updated 03-26-2016

Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage. Just think about Amazon's recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions. It's a marketing machine, and its big data analytics capabilities have made it extremely successful. The ability to analyze big data provides unique opportunities for your organization as well. You'll be able to expand the kind of analysis you can do. Instead of being limited to sampling large data sets, you can now use much more detailed and complete data to do your analysis. However, analyzing big data can also be challenging. Changing algorithms and technology, even for basic data analysis, often has to be addressed with big data. The first question that you need to ask yourself before you dive into big data analysis is what problem are you trying to solve? You may not even be sure of what you are looking for. You know you have lots of data that you think you can get valuable insight from. And certainly, patterns can emerge from that data before you understand why they are there. If you think about it though, you're sure to have an idea of what you're interested in. For instance, are you interested in predicting customer behavior to prevent churn? Do you want to analyze the driving patterns of your customers for insurance premium purposes? Are you interested in looking at your system log data to ultimately predict when problems might occur? The kind of high-level problem is going to drive the analytics you decide to use. Alternately, if you're not exactly sure of the business problem you're trying to solve, maybe you need to look at areas in your business that need improvement. Even an analytics-driven strategy — targeted at the right area — can provide useful results with big data. When it comes to analytics, you might consider a range of possible kinds, which are briefly outlined in the table. Analysis Type Description Basic analytics for insight Slicing and dicing of data, reporting, simple visualizations, basic monitoring. Advanced analytics for insight More complex analysis such as predictive modeling and other pattern-matching techniques. Operationalized analytics Analytics become part of the business process. Monetized analytics Analytics are utilized to directly drive revenue.

Article / Updated 03-26-2016

Many companies are exploring big data problems and coming up with some innovative solutions. Now is the time to pay attention to some best practices, or basic principles, that will serve you well as you begin your big data journey. In reality, big data integration fits into the overall process of integration of data across your company. Therefore, you can't simply toss aside everything you have learned from data integration of traditional data sources. The same rules apply whether you are thinking about traditional data management or big data management. Keep these key issues at the top of your priority list for big data integration: Keep data quality in perspective. Your emphasis on data quality depends on the stage of your big data analysis. Don't expect to be able to control data quality when you do your initial analysis on huge volumes of data. However, when you narrow down your big data to identify a subset that is most meaningful to your organization, this is when you need to focus on data quality. Ultimately, data quality becomes important if you want your results to be understood n context with your historical data. As your company relies more and more on analytics as a key planning tool, data quality can mean the difference between success and failure. Consider real-time data requirements. Big data will bring streaming data to the forefront. Therefore, you will have to have a clear understanding of how you integrate data in motion into your environment for predictable analysis. Don't create new silos of information. While so much of the emphasis around big data is focused on Hadoop and other unstructured and semi-structured sources, remember that you have to manage this data in context with the business. You will therefore need to integrate these sources with your line of business data and your data warehouse.

Article / Updated 03-26-2016

As you enter the world of big data, you'll need to absorb many new types of database and data-management technologies. Here are the top-ten big data trends: Hadoop is becoming the underpinning for distributed big data management. Hadoop is a distributed file system that can be used in conjunction with MapReduce to process and analyze massive amounts of data, enabling the big data trend. Hadoop will be tightly integrated into data warehousing technologies so that structured and unstructured data can be integrated more effectively. Big data makes it possible to leverage data from sensors to change business outcomes. More and more businesses are using highly sophisticated sensors on the equipment that runs their operations. New innovations in big data technology are making it possible to analyze all this data to get advanced notification of problems that can be fixed to protect the business. Big data can help a business initiative become a real-time action to increase revenue. Companies in markets such as retail are using real-time streaming data analytics to keep track of customer actions and offer incentives to increase revenue per customer. Big data can be integrated with historical data warehouses to transform planning. Big data can provide a company with a better understanding of massive amounts of data about their business. This information about the current state of the business can be combined with historical data to get a full view of the context for business change. Big data can change the way diseases are managed by adding predictive analytics. Increasingly, healthcare practitioners are looking to big data solutions to gain insights into disease by compare symptoms and test results to databases of results from hundreds of thousands of other cases. This allows practitioners to more quickly predict outcomes and save lives. Cloud computing will transform the way that data will be managed in the future. Cloud computing is invaluable as a tool to support the expansion of big data. Increasingly, cloud services that are optimized for data will mean that many more services and delivery models will make big data more practical for companies of all sizes. Security and governance will be the difference between success and failure of businesses leveraging big data. Big data can be a huge benefit, but it isn't risk-free. Companies will discover that if they are not careful, it is possible to expose private information through big data analysis. Companies need to balance the need to analyze results with best practices for security and governance. Veracity, or truthfulness, of big data will become the most important issue for the coming year. Many companies can get carried away with the ability to analyze massive amounts of data and get back compelling results that predict business outcomes. Therefore, companies will find that the truthfulness of the data must become a top priority or decision making will suffer. As big data moves out of the experimental stage, more packaged offerings will be developed. Most big data projects initiated over the past few years have been experimental. Companies are cautiously working with new tools and technology. Now big data is about to enter the mainstream. Lots of packaged big data offerings will flood the market. Use cases and new innovative ways to apply big data will explode. Early successes with big data in different industries such as manufacturing, retail, and healthcare will lead to many more industries looking at ways to leverage massive amounts of data to transform their industries.

Article / Updated 03-26-2016

To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must include a variety of services that enable companies to make use of myriad data sources in a fast and effective manner. Here's a closer look at what's in the image and the relationship between the components: Interfaces and feeds: On either side of the diagram are indications of interfaces and feeds into and out of both internally managed data and data feeds from external sources. To understand how big data works in the real world, start by understanding this necessity. What makes big data big is that it relies on picking up lots of data from lots of sources. Therefore, open application programming interfaces (APIs) will be core to any big data architecture. In addition, keep in mind that interfaces exist at every level and between every layer of the stack. Without integration services, big data can't happen. Redundant physical infrastructure: The supporting physical infrastructure is fundamental to the operation and scalability of a big data architecture. Without the availability of robust physical infrastructures, big data would probably not have emerged as such an important trend. To support an unanticipated or unpredictable volume of data, a physical infrastructure for big data has to be different than that for traditional data. The physical infrastructure is based on a distributed computing model. This means that data may be physically stored in many different locations and can be linked together through networks, the use of a distributed file system, and various big data analytic tools and applications. Security infrastructure: The more important big data analysis becomes to companies, the more important it will be to secure that data. For example, if you are a healthcare company, you will probably want to use big data applications to determine changes in demographics or shifts in patient needs. This data about your constituents needs to be protected both to meet compliance requirements and to protect the patients' privacy. You will need to take into account who is allowed to see the data and under what circumstances they are allowed to do so. You will need to be able to verify the identity of users as well as protect the identity of patients. Operational data sources: When you think about big data, understand that you have to incorporate all the data sources that will give you a complete picture of your business and see how the data impacts the way you operate your business. Traditionally, an operational data source consisted of highly structured data managed by the line of business in a relational database. But as the world changes, it is important to understand that operational data now has to encompass a broader set of data sources.