Dell’s 2021 Server Trends & Observations

With the start of a new year, we can say goodbye to the tumultuous and challenging 2020 – a year that brought about monumental changes in our industry through acquisitions, technology introductions, and of course, a shift to remote work force. No one could have predicted all the changes that happened last year, but now we have an opportunity to look back on how the server trends and technologies detailed by us last year impacted our industry. And as we have done for the past several years, we want to continue our tradition in Dell’s Infrastructure CTO group of highlighting some of the most interesting technology and industry trends we expect to see impacting our server incubation and product efforts this year. These trends were compiled by polling our senior technologists in the server organization, who are looking at the most impactful influences to their workstreams.

When the technologists provided their inputs, the underlying theme that emerged was the desire to life cycle data – curate, transport, analyze and preserve data – in the most effective and secure means, while producing the most efficient business outcomes from the infrastructure. Since the generation of data has continued to increase, customers are looking for ways to leverage third-party services in an integrated offering to allow them to understand how to more quickly analyze and value the right data in the most cost-effective and secure manner. This paradigm has also forced owners of the IT equipment that performs these analyses to ensure they are using the most effective technology integrations. These integrations need to be managed with the minimal amount of operational expenses across a continuum of Edge, Core and Cloud architectures. Finally, 2020 created another challenge to carry forward, which is the ability to adopt new technologies with more remote staff and remote users stressing the infrastructure in means and methods not expected this early in most digital transformation plans.

So, with that introduction, let us provide you the Top Trends for 2021 that are influencing our server technology efforts and product offerings:

  1. aaS becomes the Enterprise theme. As technology velocity continues to rise, enterprise customers deal with constrained budgets and legacy skillsets while still needing to focus on differentiated business outcomes with the most beneficial price/performance and least amount of bring up and maintenance overhead. The options for on-prem Infrastructure aaS offerings allow customers to be nimble and focus on their business value through diverse deployments while maintaining their data security and governance with trusted infrastructure.

  1. Server Growth is Building Vertically. As customers look for the most efficient outcomes from their infrastructure, the industry will continue to see more verticalization and specialization of offerings. Integrated solutions will address packaging and environmental considerations; SW ecosystem enablement and domain-specific accelerators address unique performance and feature requirements that are optimized for specific business outcomes.
  1. More Data, More Smarts. The challenges with data velocity, volume and volatility continue and require continuance of AI/ML adoption for analytics while increased focus on solving data life cycling challenges arises. The integration of data curation models, transport methods, preservation and security architectures with faster analysis will all be key to support and monetize the Internet of Behaviors.
  1. The Emergence of the Self-Driving Server. Customers will start seeing the use of telemetry, analytics, and policies to achieve higher levels of automation in their systems management infrastructure. Similar to the driver assist/autonomy levels of autonomous vehicles, AI Ops capabilities with systems management will usher in the era of moving automated tasks to automated decisions, with implementations showing up in addressing runaway system power and policy advising recommendation engines.
  1. Goodbye, SW Defined. Hello, SW Defined with HW Offload. Application architectures are evolving to create control plane and data plane separation. Control planes stays as a software layer and the data planes move to programmable hardware in the form of service processor add-in-cards which allow bare-metal and containerized applications to run with disaggregated infrastructure software (network virtualization, storage virtualization, GPU virtualization, security services), creating Intent Based Computing for customer workloads.
  1. 5G is Here! Seriously, it is this year. After several years of hype and promises, we will see the proliferation of 5G and with it will come shifts in paradigms around communication infrastructure, remote management models and connectivity that impact server form-factors and features. As businesses develop more edge infrastructure to handle the generation and influx of data, 5G will create the need for customers to reevaluate their edge connectivity and infrastructure management offerings to take advantage of 5G capabilities.
  1. Rethinking Memory and Storage to be Data Centric. The industry is moving from Compute-Centric Architectures to Data Centric Architectures and that transition is driving new server connected memory and storage models for IT. Technologies around persistent, encrypted and tiered memory inside the server along with remotely accessed SCM and NVMe oF data through new industry fabric standards are creating innovative IT architectures for optimal data security, scaling and preservation.
  1. Adopting new Server technology while Being Remote. The world has changed, and businesses have been forced to not just map a digital transformation but realize it to operate. Companies dealing with faster digital transitions of tools, processes and infrastructure need to operate with a remote work force. This transition is forcing companies to evaluate new server technologies and assess resource requirements which will emphasize the necessity to utilize server capabilities around debug, telemetry and analytics in a remote fashion to keep business continuity going forward.
  1. It’s not a CPU Competition, it’s a Recipe Bake-off. The processor landscape is changing, and it is becoming an environment of acquisitions, specializations and vendor differentiated integrations. We see Intel, AMD and Nvidia all making acquisitions to provide each with CPUs, DPUs and Accelerators in their portfolio. The real winner will be able to leverage their portfolio of silicon products and software libraries to form recipes of integrated offerings for targeted workloads to help end customers optimize business outcomes.
  1. Measure your IT Trust Index. Security around server access and data protection has never been more challenging, so customers need to be able to quantify their security confidence in order to gauge infrastructure trustworthiness and identify digital risks. Customers need to analyze product origins and features, new security technologies and segment specific digital threats in the backdrop of increasing regulatory landscape to formulate their measurement of IT trust from the Edge to Core to Cloud.

As we focus on the new year, new technologies and new initiatives, we welcome the opportunity to go deeper with each of these trends with our customers and their specific views and challenges. Reach out to your account team for an engagement and follow us on Twitter to stay up to date.

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    Data determines the winners and losers in the digital age If we examine the top trends many organizations are focused on today—harnessing big data and analytics, embracing the Internet of Things, investing in artificial intelligence—they all have a common foundation. Data. It’s data that powers digital transformation and the digital economy. The organizations best positioned to win in this data era are those who have superior strategies for collecting and harnessing the untapped potential locked away in this ever-growing ocean of data. Unstructured data driving data sprawl Unstructured data is driving much of this growth. Gartner … READ MORE

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    The Evolution of the Data Warehouse in the Big Data Era

    About 20 years ago, I started my journey into data warehousing and business analytics. Over all these years, it’s been interesting to see the evolution of big data and data warehousing, driven by the rise of artificial intelligence and widespread adoption of Hadoop. When I started in this work, the main business challenge was how to handle the explosion of data with ever-growing data sets and, most importantly, how to gain business intelligence in as close to real time as possible. The effort to solve these business challenges led the way for a ground-breaking architecture called … READ MORE

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    COVID-19 Impact: Temporary Surge in Sales of Big Data Analytics in Automotive Product Observed …

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    How a Unified Approach Supports Your Data Strategy

    Are you finding it easy to explore and analyze data located on-premise or in the cloud? You are not alone, but there is a solution.

    It’s a rare instance of a company that stores 100 percent of its data in one place or a company that secures 100 percent of its data in the cloud. Most companies must combine datasets. But by establishing a unified data tier, it can be easier to perform certain types of analytics, especially when the data is widely distributed.

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    Take for example the case of a bike-share system that looked at its publicly available ridership data, then added weather data to predict bike ridership and made appropriate changes to make sure bikes were available when and where riders needed them. If the data was stored in different geographical areas and used different storage systems, it might be difficult to compare that information to make an informed decision.

    So how can companies take advantage of data, whether it’s located in Oracle Autonomous Data Warehouse, Oracle Database, object store, or Hadoop? A recent Oracle webcast titled, “Explore, Access, and Integrate Any Data, Anywhere,” explored this issue. Host Peter Jeffcock outlined four new services Oracle released in February 2020 to let companies dive right in and solve these real-world problems, manage data, and enable augmented analytics:

    The idea is that there needs to be a unified data tier that starts with workload portability, which means that your data and the data environment can be managed in the public cloud, on a local cloud, or in your on-premise data store.

    Unified Data Tier

    The next step is to develop a converged database, especially with an autonomous component so that repeatable processes free up administrative time and reduce human error. Oracle Database allows for multiple data models, multiple workloads, and multiple tenants, making it easier to operate because all these processes are managed into a single database.

    You can take it one step further if you add the cloud to the configuration. Oracle can manage the data and apply different processes and machine learning so that you can run your database autonomously in the cloud.

    Unified Data Tier

    The unified data tier also means taking advantage of multiple data stores such as data lakes and other databases. And finally expanding that ecosystem with our partners such as our recent agreement with Microsoft that allows for a unified data tier between Oracle Cloud and Microsoft’s Azure.

    “If you want to run an application in the Microsoft Cloud and you want to connect to the Oracle Cloud where the data is stored, that’s now supported. It’s a unique relationship and it’s something to look into if you want to run a multi-cloud strategy,” Jeffcock says.

    You can experience the full presentation if you register for the on-demand webcast.

    To learn more about how to get started with data lakes, check out Oracle Big Data Service—and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox. Also, follow us on Twitter @OracleBigData.

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    WHO launches Blockchain platform to combat COVID19

    As the world faces the ongoing deadly coronavirus pandemic, government around the world are looking for alternative tools in order to contain the spread of the virus.

    Authorities are now looking towards tech and blockchain companies to help them track data from health workers. Authorities are looking to use this data in order to create a map that will help them track people who have high risk of exposure and infection.

    The World Health Organization, and the United States Center for Disease Control, along with other international agencies are now looking towards IBM’s Blockchain Platform. Authorities said that IBM’s platform will provide the necessary support for them to be able to stream data into the MiPasa Project.

    Information from the World Health Organization, the Center for Disease Control, and other similar agencies said that IBM’s Blockchain Platform would support a data streaming service for the MiPasa Project.

    IBM has been engaged by purpose-driven entities, having meaningful projects like MiPasa that are designed to have an impact on the outcomes during this time of crisis.

    MiPasa: Integrating data at scale

    The MiPasa Blockchain technology uses big data analytics to analyze data provided by health workers on the Covid-19 pandemic.

    The WHO press release revealed that the blockchain platform was made to ease the synthesizing of data sources. It is designed to address inconsistencies and identify errors or misreporting. The new platform also allows the integration of trusted new information.

    Furthermore, the creators of Mipasa hope that this tool can help technologists, data scientists, and public health officials by giving them the data they need at scale to respond. It is also expected to help in formulating solutions useful in controlling the covid-19 pandemic.

    The blockchain-based platform is slated to host an array soon of publicly accessible analytics tools too. Mipasa describes the new platform’s reliability and accessibility as a “verifiable information highway.”

    Officials help the Mipasa platform and vice versa

    The Mipasa platform is supported by a variety of professionals in many specialized fields, including health, software and app development, and data privacy, in making it easy to gather reliable and quality data. The group aims to make the data accessible to appropriate entities that support Mipasa.

    The onboarding is done through the Unbounded Network, which is running a production version of The Linux Foundation’s Foundation’s Hyperledger Fabric on multiple clouds, IBM has been amongst the early supporters.

    IBM helps more participants to collaborate openly, through permissioned and non-permissioned blockchains and has been in production since 2018. The blockchain-based platform was created for the attested coronavirus data built on Hyperledger Fabric.

    MiPasa can already access information from agencies that integrates their platform with the simple use of APIs. These organizations include the World Health Organization, the Center for Disease Control, the Israeli Public Health Ministry, and other qualified agencies.

    The WHO believes that the study, collate, and collection of Covid data, including spread and containment, is much easier with the use of the MiPasa platform. The project is useful in monitoring and forecasting local and global trends about the pandemic. The WHO also believes that the MiPasa project helps detect asymptomatic carriers through sharing big data on infection records and occurrences globally to powerful AI processors around the globe.

    MiPasa was launched with the collaboration of private companies, including IBM, Oracle and Microsoft, and other supporters like and the John Hopkins University. A robust data platform lays a foundation for helping to solve many other problems; MiPasa is starting to get off the ground.

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    Data Lakes: Examining the End to End Process

    It’s a good way to think of a data lake as being the ultimate hub for your organization. On the most basic level, it takes data in from various sources and makes it available for users to query. But much more goes on during the entire end to end process involving a data lake. To get a clearer understanding of how it all comes together—and a bird’s-eye view of what it can do for your organization—let’s look at each step in depth.

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    Step 1: Identify and connect sources

    Unlike data warehouses, data lakes can take inputs from nearly any type of source. Structured, unstructured, and semi-structured data can all coexist in a data lake. The primary goal of this type of feature is allowing all of the data to exist in a single repository in its raw format. A data warehouse specializes in housing processed and prepared data for use, and while that is certainly helpful in many instances, it still leaves many types of data out of the equation. By unifying these disparate data sources into a single source, a data lake allows users to have access to all types of data without requiring the logistical legwork of connecting to individual data warehouses.

    Step 2: Ingest data into zones

    If a data lake is set up per best practices, then incoming data will not just get dumped into a single data swamp. Instead, since the data sources come from known quantities, it is possible to establish landing zones for datasets from particular sources. For example, if you know that a dataset contains sensitive financial information, it can immediately go into a zone that limits access by user role and additional security measures. If it’s data that comes in a set format ready for use by a certain user group (for example, the data scientists in HR), then that can immediately go into a zone defined for that. And if another dataset delivers raw data with minimal metadata specifics to easily identify it on a database level (like a stream of images), then that can go into its own zone of raw data, essentially setting that group aside for further processing.

    In general, it’s recommended that the following zones be used for incoming data. Establishing this zone sorting right away allows for the first broad strokes of organization to be completed without any manual intervention. There are still more steps to go to optimize discoverability and readiness, but this automates the first big step. Per our blog post 6 Ways To Improve Data Lake Security, these are the recommended zones to establish in a data lake:

    Temporal: Where ephemeral data such as copies and streaming spools live prior to deletion.

    Raw: Where raw data lives prior to processing. Data in this zone may also be further encrypted if it contains sensitive material.

    Trusted: Where data that has been validated as trustworthy lives for easy access by data scientists, analysts, and other end users.

    Refined: Where enriched and manipulated data lives, often as final outputs from tools.

    Step 3: Apply security measures

    Data arrives into a data lake completely raw. That means that any inherent security risk with the source data comes along for the ride when it lands in the data lake. If there’s a CSV file with fields containing sensitive data, it will remain that way until security steps have been applied. If step 2 has been established as an automated process, then the initial sorting will help get you halfway to a secure configuration.

    Other measures to consider include:

    • Clear user-based access defined by roles, needs, and organization.
    • Encryption based on a big-picture assessment of compatibility within your existing infrastructure.
    • Scrubbing the data for red flags, such as known malware issues, suspicious file names or formats (such as an executable file living in a dataset that is otherwise media files). Machine learning can significantly speed up this process.

    Running all incoming data through a standardized security process ensures consistency among protocols and execution; if automation is involved, this also helps to maximize efficiency. The result? The highest levels of confidence that your data will go only to the users that should see it.

    Step 4: Apply metadata

    Once the data is secure, that means that it’s safe for users to access it—but how will they find it? Discoverability is only enabled when the data is properly organized and tagged with metadata. Unfortunately, since data lakes take in raw data, data can arrive with nothing but a filename, format, and time stamp. So what can you do with this?

    A data catalog is a tool that can work with data lakes in a way that optimizes discovery. By enabling more metadata application, data can be organized and labeled in an accurate and effective way. In addition, if machine learning is utilized, the data catalog can begin recognizing patterns and habits to automatically label things. For example, let’s assume a data source is consistently sending MP3 files of various lengths—but the ones over twenty minutes are always given the metatag “podcast” after arriving in the data lake. Machine learning will pick up on that pattern and then start auto-tagging that group with “podcast” upon arrival.

    Given that the volume of big data is getting bigger—and that more and more sources of unstructured data are entering data lakes, that type of pattern learning and automation can make huge differences in efficiency.

    Step 5: User discovery

    Once data is sorted, it’s ready for users to discover. With all of those data sources consolidated into a single data lake, discovery is easier than ever before. If tools like analytics exist outside of the data lake’s infrastructure, then there’s only one export/import step that needs to take place for the data to be used. In a best-case scenario, those tools are integrated into the data lake, allowing for real-time queries against the absolute latest data, all without any manual intervention.

    Why is this so important? A recent survey showed that, on average, five data sources are consulted before making a decision. Consider the inefficiency if each source has to be queried and called manually. Putting it all in a single accessible data lake and integrating tools for real-time data querying removes numerous steps so that discovery can be as easy as a few clicks.

    The Hidden Benefits of a Data Lake

    The above details break down the end-to-end process of a data lake—and the resulting benefits go beyond saving time and money. By opening up more data to users and removing numerous access and workflow hurdles, users have the flexibility to try new perspectives, experiment with data, and look for other results. All of this leads to previously impossible insights, which can drive an organization’s innovation in new and unpredictable ways.

    To learn more about how to get started with data lakes, check out Oracle Big Data Service—and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

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    Manufacturing & Industrial Automation Lead The Way

    I’m always surprised that some people think of manufacturing as stodgy, old school and slow to change – in my view, nothing could be further from the truth! All the evidence shows that the manufacturing industry has consistently led the way from mechanical production, powered by steam in the 18th century, to mass production in the 19th century, followed by 20th century automated production.

    The data center merging with the factory floor

    Fast forward to today. The fourth industrial revolution is well underway, driven by IoT, edge computing, cloud and big data. And once again, manufacturers are at the forefront of intelligent production, leading the way in adopting technologies like augmented reality, 3D printing, robotics, artificial intelligence, cloud-based supervisory control and data acquisition systems (SCADA) plus programmable automation controllers (PACs). Watch the video below that addresses how manufacturers are changing to embrace Industry 4.0.

    In fact, I always visualize the fourth industrial revolution, otherwise known as Industry 4.0, as the data center merging with the factory floor, where you have the perfect blend of information and operational technology working together in tandem. Let’s look at a couple of examples.

    Helping monitor and manage industrial equipment

    One of our customers, Emerson, a fast-growing Missouri-based company with more than 200 manufacturing locations worldwide, provides automation technology for thousands of chemical, power, and oil & gas organizations around the world. Today, Emerson customers are demanding more than just reliable control valves. They need help performing predictive maintenance on those valves.

    To address these needs, Emerson worked with Dell Technologies OEM | Embedded & Edge Solutions to develop and deploy an industrial automation solution that collects IoT data to help its customers better monitor, manage and troubleshoot critical industrial equipment. With our support, Emerson successfully developed a new wireless-valve monitoring solution and brought it to market faster than the competition. This is just the first step in what Emerson sees as a bigger journey to transform services across its entire business. You can read more about our work together here.

    Bringing AI to the supply chain to reduce waste and energy

    Meanwhile, San-Francisco based Noodle.ai has partnered with us to deliver the world’s first “Enterprise AI” data platform for manufacturing and supply chain projects.

    This solution allows customers to anticipate and plan for the variables affecting business operations, including product quality, maintenance, downtime, costs, inventory and flow. Using AI, they can mitigate issues before they happen, solve predictive challenges, reduce waste and material defects as well as cutting the energy required to create new products.

    For example, one end-customer, a $2 billion specialty steel manufacturer, needed to increase profit per mill hour, meet increasing demand for high quality steel at predictable times, and reduce the amount of energy consumed. Using the “Enterprise AI” data platform, the customer reported $80 million savings via reduced energy costs, freight costs, scrapped product, and raw material input costs.

    Helping design innovative and secure voting technology

    Yet, another customer, Democracy Live wanted to deliver a secure, flexible, off-the-shelf balloting device that would make voting accessible to persons with disabilities and that could replace outdated, proprietary and expensive voting machines.

    After a comprehensive review of vendors and products, Democracy Live asked us to design a standardized voting tablet and software image. Our Dell Latitude solution complete with Intel processors and pre-loaded with Democracy Live software and Windows 10 IoT Enterprise operating system provides strong security and advanced encryption.

    And the good news for Democracy Live that we take all the headaches away by managing the entire integration process, including delivery to end-users. The result? Secure, accessible voting with up to 50 percent savings compared with the cost of proprietary voting machines. Read what Democracy Live has to say about our collaboration here.

    Change is constant

    Meanwhile, the revolution continues. Did you know that, according to IDC, by the end of this year 60 percent of plant workers at G2000 manufacturers will work alongside robotics, while 50 percent of manufacturing supply chains will have an in-house or outsourced capability for direct-to-consumption shipments and home delivery? More details available here.

    Unlock the power of your data

    Don’t get left behind! Dell Technologies OEM | Embedded & Edge Solutions is here to help you move through the digital transformation journey, solve your business challenges and work with you to re-design your processes. We can help you use IoT and embedded technologies to connect machines, unlock the power of your data, and improve efficiency and quality on the factory floor.

    And don’t forget we offer the broadest range of ruggedized and industrial grade products, designed for the most challenging environments, including servers, edge computing, laptops and tablets. We’d love to hear from you – contact us here and do stay in touch.

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    Four Tools to Integrate into Your Data Lake

    A data lake is an absolutely vital piece of today’s big data business environment. A single company may have incoming data from a huge variety of sources, and having a means to handle all of that is essential. For example, your business might be compiling data from places as diverse as your social media feed, your app’s metrics, your internal HR tracking, your website analytics, and your marketing campaigns. A data lake can help you get your arms around all of that, funneling those sources into a single consolidated repository of raw data.

    But what can you do with that data once it’s all been brought into a data lake? The truth is that putting everything into a large repository is only part of the equation. While it’s possible to pull data from there for further analysis, a data lake without any integrated tools remains functional but cumbersome, even clunky.

    On the other hand, when a data lake integrates with the right tools, the entire user experience opens up. The result is streamlined access to data while minimizing errors during export and ingestion. In fact, integrated tools do more than just make things faster and easier. By expediting automation, the door opens to exciting new insights, allowing for new perspectives and new discoveries that can maximize the potential of your business.

    To get there, you’ll need to put the right pieces in place. Here are four essential tools to integrate into your data lake experience.

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    Machine Learning

    Even if your data sources are vetted, secured, and organized, the sheer volume of data makes it unruly. As a data lake tends to be a repository for raw data—which includes unstructured items such as MP3 files, video files, and emails, in addition to structured items such as form data—much of the incoming data across various sources can only be natively organized so far. While it can be easy to set up a known data source for, say, form data into a repository dedicated to the fields related to that format, other data (such as images) arrives with limited discoverability.

    Machine learning can help accelerate the processing of this data. With machine learning, data is organized and made more accessible through various processes, including:

    In processed datasets, machine learning can use historical data and results to identify patterns and insights ahead of time, flagging them for further examination and analysis.

    With raw data, machine learning can analyze usage patterns and historical metadata assignments to begin implementing metadata automatically for faster discovery.

    The latter point requires the use of a data catalog tool, which leads us to the next point.

    Data Catalog

    Simply put, a data catalog is a tool that integrates into any data repository for metadata management and assignment. Products like Oracle Cloud Infrastructure Data Catalog are a critical element of data processing. With a data catalog, raw data can be assigned technical, operational, and business metadata. These are defined as:

    • Technical metadata: Used in the storage and structure of the data in a database or system
    • Business metadata: Contributed by users as annotations or business context
    • Operational metadata: Created from the processing and accessing of data, which indicates data freshness and data usage, and connects everything together in a meaningful way

    By implementing metadata, raw data can be made much more accessible. This accelerates organization, preparation, and discoverability for all users without any need to dig into the technical details of raw data within the data lake.

    Integrated Analytics

    A data lake acts as a middleman between data sources and tools, storing the data until it is called for by data scientists and business users. When analytics and other tools exist separate from the data lake, that adds further steps for additional preparation and formatting, exporting to CSV or other standardized formats, and then importing into the analytics platform. Sometimes, this also includes additional configuration once inside the analytics platform for usability. The cumulative effect of all these steps creates a drag on the overall analysis process, and while having all the data within the data lake is certainly a help, this lack of connectivity creates significant hurdles within a workflow.

    Thus, the ideal way to allow all users within an organization to swiftly access data is to use analytics tools that seamlessly integrate with your data lake. Doing so removes unnecessary manual steps for data preparation and ingestion. This really comes into play when experimenting with variability in datasets; rather than having to pull a new dataset every time you experiment with different variables, integrated tools allow this to be done in real time (or near-real time). Not only does this make things easier, this flexibility opens the door to new levels of insight as it allows for previously unavailable experimentation.

    Integrated Graph Analytics

    In recent years, data analysts have started to take advantage of graph analyticsthat is, a newer form of data analysis that creates insights based on relationships between data points. For those new to the concept, graph analytics considers individual data points similar to dots in a bubble—each data point is a dot, and graph analytics allows you to examine the relationship between data by identifying volume of related connections, proximity, strength of connection, and other factors.

    This is a powerful tool that can be used for new types of analysis in datasets with the need to examine relationships between data points. Graph analytics often works with a graph database itself or through a separate graph analytics tool. As with traditional analytics, any sort of extra data exporting/ingesting can slow down the process or create data inaccuracies depending on the level of manual involvement. To get the most out of your data lake, integrating cutting-edge tools such as graph analytics means giving data scientists the means to produce insights as they see fit.

    Why Oracle Big Data Service?

    Oracle Big Data Service is a powerful Hadoop-based data lake solution that delivers all of the needs and capabilities required in a big data world:

    • Integration: Oracle Big Data Service is built on Oracle Cloud Infrastructure and integrates seamlessly into related services and features such as Oracle Analytics Cloud and Oracle Cloud Infrastructure Data Catalog.
    • Comprehensive software stack: Oracle Big Data Service comes with key big data software: Oracle Machine Learning for Spark, Oracle Spatial Analysis, Oracle Graph Analysis, and much more.
    • Provisioning: Deploying a fully configured version of Cloudera Enterprise, Oracle Big Data Service easily configures and scales up as needed.
    • Secure and highly available: With built-in high availability and security measures, Oracle Big Data Service integrates and executes this in a single click.

    To learn more about Oracle Big Data Service, click here—and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

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