What is Big Data?

The digital revolution has generated an abundance of diverse data. By processing complex information with big data analytics,
organizations and enterprises can obtain valuable insights to drive growth. Standing at the forefront of memory solutions,
Samsung is advancing the adoption of big data in diverse fields. From high performance computing to innovative cloud computing and data center systems, its comprehensive portfolio empowers organizations to leverage cutting-edge analytics to reach their goals.

A visionary illustration of Big Data.

What is Big Data?

Data is no longer limited to organized information stored in columns and rows. As digital devices become more embedded in our daily lives, the volume and types of data available are growing at an exponential pace. With the development of advanced analytics, computers can now automatically spot patterns in new data sources. From audio and visual recordings to sensory information, almost anything can be turned into data. In the era of big data, creating efficient solutions to store, organize and analyze these complex information holds the key to uncovering meaningful trends and predictions.

An illustrative image of vast amounts of data combines together in one stream.

Data-driven Growth

Understanding customer behaviors and monitoring internal operations are key priorities for many businesses. The emergence of big data enables companies to track their progress with precision. Whether it’s boosting sales or improving efficiency, in-depth analytics can help enterprises develop strategies to gain a competitive edge.

Leveraging Big Data for business growth calls for comprehensive investments in diverse areas. In addition to reorganizing the IT infrastructure, new skills will also be required to support and operate the technology.

An illustrative image of people reviewing analytics.

Optimizing Knowledge Transfer

Businesses need to create and disseminate new knowledge within the organization to boost competitiveness and the efficiency of the knowledge transfer process can determine the success or failure of projects and strategies.

In addition to discovering new trends, big data systems can enhance the distribution of new insights. With more control over the knowledge transfer process, enterprises can ensure key stakeholders are fully informed when making decisions.

An illustrative image of grid matrix against an image if cityscape.

Maximizing the World of Data

A large proportion of the data in the world are either unstructured or semi-structured. To generate maximum value from big data, unstructured content needs to be combined with structured information for analysis. For many enterprises, this means integrating advanced analytics with existing intelligence capabilities, storage systems, and data structure.

From customer analytics to risk management, expanding the variety of data points will take the guesswork out of strategizing, optimizing overall operations.

An illustrative image of cloud in digital form.

Developing a Tailored Cloud Strategy

Building a tailored cloud strategy to fit organizational needs and goals can transform the efficacy of big data integration. As advanced analytics require a wide range of resources to operate, the cloud infrastructure needs to be engineered to optimize data flow. In addition, scalability of the cloud architecture should also be taken into consideration to ensure long-term sustainability.

  • An infographic of Hadoop ecosystem.

    The Heart of Big Data Analytics

    Hadoop is an open-source software framework that’s synonymous with big data storage and analysis. The system’s ability to store and process a wide range of data makes it ideal for supporting advanced analytics, such as predictive analytics, data mining, and machine learning.

    Hadoop consists of four modules, each designed to perform specialized tasks for big data analytics. The Distributed File System allows fast data access across a large number of storage devices, while MapReduce enables efficient data set conversions. With Hadoop Common, different computer operating systems can retry data stored in Hadoop. Finally, YARN takes care of allocating system resources.

  • A comparison infographic of cloud computing and edge computing. Edge computing has lower network traffic and connectivity costs.

    Efficient Data Processing

    IoT devices collect an enormous amount of data, which takes time and resources to process in a cloud-based, centralized system.
    Since not all data collected are usable, this system can be inefficient.

    By sending only the most relevant information to the cloud, edge computing can vastly reduce the workload of data centers and
    cloud computing facilities. In addition to enhancing efficiency, edge computing can also lower network traffic and connectivity costs.

  • A comparison infographic of In-memory databases and On-disk databases. An in-memory database stores data in a RAM and offers the fastest access speed.

    Accelerating Data Access

    An in-memory database stores data in a RAM instead of a hard drive. As there is no disk to slow down the process,
    this offers the advantage of faster access speeds and enables real-time updates.

    In-memory databases also solve the difficulty of storing, transferring and processing large amounts of unstructured data.

  • An infographic shows the process of making predictions in 4 steps; collect data, clean data, identify patterns, make predictions.

    Looking Into the Future

    As its name suggests, predictive analytics makes predictions about future events based on the analysis of historical data. It uses a wide range of techniques from data mining, statistics, modeling, and artificial intelligence to capture patterns and trends in datasets. For businesses and enterprises, these relationships can be utilized to identify future risks and opportunities, including predictive maintenance, financial analysis, and quality assurance.

    The accuracy of predictive analytics will improve as artificial intelligence advances, allowing organizations to be more proactive and forward-thinking.

  • A comparison infographic of centralized and blockchain-based storage system. The blockchain-based storage system offer greater transparency and security.

    Seamless Yet Secure Data Storage

    By storing information in multiple computers rather than a centralized server, blockchain-based storage systems offer greater transparency and security. The decentralized nature of the technology prevents a single authority from altering transactions and data, preserving the integrity of the information recorded. In the context of big data, blockchain provides a more secure solution to store sensitive information, such as medical records, as the dataset cannot be taken from a single location.

  • A comparison infographic of Streaming analytics. Stream processing contains Hadoop, Open Source R, TERR, SAS, MATLAB, In database analytics, and Spark.

    Real-time Analysis

    The main advantage of streaming analytics is that it allows organizations to process data as soon as it’s available. It’s ideally suited for enhancing decision making in time-sensitive situations as organizations can rapidly connect multiple events and incidents to identify risks or opportunities.

  • An infographic shows the process of artificial intelligence (AI). The AI process unstructured data with multi-layered neural networks and analyze complex information.

    A Smarter Future

    While Artificial Intelligence is not a new concept, its advances in recent years are vastly expanding the capabilities of computers and mobile devices. As a subset of AI, machine learning gives computers the ability to learn, evolve, and improve by themselves. Combined with the availability of big data, this process can extract patterns in historical data and build models to predict future outcomes.

    By processing unstructured data with multi-layered neural networks, deep learning makes it possible for computers to analyze complex information, such as visual content and spoken words. This greatly increases the types of data organizations can track and analyze.

An image of next-generation servers.

Next-Generation Servers

To process vast amounts of data, organizations need servers that deliver ultra-fast speed and superior performance. In an era where analytics are advancing at a rapid pace, traditional servers with fixed hardware configurations may be unsustainable. For businesses to truly maximize the potential of big data, adopting flexible server solutions is a must.

An image of monitor.

Superior Computing Performance

As the next generation of technologies such as IoT and AI continue to advance, the data enterprises have to process will grow in size and complexity. By networking compute servers together in clusters, a high-performance computing architecture (HPC) delivers the required speed, stability, and performance to process massive amounts of data. Servers in the network run programs and algorithms, while a storage unit feeds the data and captures the output. In an HPC architecture, all components must be able to support high-speed data transfer to maximize performance.

An image of various IT devices, including laptop, smartphone and tablet.

Cloud Computing

By reducing the need for in-house hardware investments, cloud computing enables organizations to flexibly adopt and deploy big data analytics. The scalability of cloud computing coupled with virtualization allows enterprises to adjust their IT strategy as priorities shift, while the centralization of data storage enables more efficient internal flow of information and insights.

Building the Foundation Big

Samsung offers a wide range of cutting-edge memory solutions designed to meet the needs of big data analytics. Combining extreme low-latency with best-in-class performance, Z-SSD is a powerful component suited for today’s in-memory databases and high-performance computing systems. With Enterprised SSD, Samsung delivers unmatched reliability and speed for advanced server needs. Originally developed for graphics processing, Samsung’s HBM2E (High Bandwidth Memory) is highly capable of identifying complex patterns in mass data for AI operations. As demands for big data solutions and cloud computing grow, RDIMM and LRDIMM bring superior performance to accelerate data-intensive applications, including real-time analytics, virtualization, and in-memory computing.

An image of big data solutions including  Z-SSD, enterprise SSDs, HBM2E, LPDDR5 and RDIMM.