Modern data analytics can be a challenge when the underlying data platform can’t keep up – why modern storage technology, is more critical now than ever. Data has never been more critical to businesses than it is today. Data drives connected cities, influencing business decisions in real-time and making every customer interaction a personal and tailored experience. However, many companies still do not even know how to use their data sources.

There is often a lack of a strategy for collecting all available data, analyzing it, and making holistic decisions based on it. It often fails to integrate the previously separate data silos into one data environment. Therefore, an adequate data storage and delivery strategy are critical to getting the most value out of the data.

Changing Data Usage Places New Demands On The Infrastructure

The global volume of data is exploding. According to one forecast, it will grow to an unimaginable 175 zettabytes by 2025. IDC made this prediction despite the events of 2020 that have caused consumer data usage to skyrocket.

The way data is used has changed. Cloud applications, mobile devices, social media platforms, and IoT ( IoT ) sensors contribute to data growth. Each of the storage workloads required for this fulfills essential tasks. Applications that rely on file storage have become more sophisticated than database applications. The technical silos typically seen between these workloads have created challenges that can no longer be ignored. Businesses are stuck without a data platform that can keep up with today’s analytics tools.

The key to better data analysis is modernizing the IT infrastructure. Companies that make targeted investments here are well-positioned for modern analysis requirements. This is the only way to derive valuable insights from big data projects and use them strategically. The aim is to improve operational efficiency and the user experience and compliance, regulatory and security processes, develop new products and generate new revenue streams.

One Step Ahead Of The Competition

In a recent research project, Pure Storage partnered with the Enterprise Strategy Group (ESG) to examine the benefits for companies investing in analytics versus those not. The study showed that companies with mature data analysis capabilities are one step ahead of the competition. Compared to companies that don’t leverage data analysis skills, these companies were 3.2x more likely to perform better on customer satisfaction, 2.4x more likely to increase revenue per employee over the past two years, and 2.7 times more likely to have had a shorter time to market.

The critical component here is a high-performance flash-based cloud-ready solution that supports fast file and object storage. This provides the technological basis to support diverse, robust, and mature analysis programs with real-time or streaming analysis. This makes it possible to achieve positive and powerful results beyond increased sales potential.

Guide To The Implementation Of A Modern Data Strategy

Pure Storage recently published a guide to help organizations achieve their goals for modern data analytics. He takes an in-depth look at five areas where data storage solutions must offer advanced capabilities to enable organizations to evolve their analytics maturity. The most important findings are:

High-performance Storage is the path to faster insights. Companies that gain faster insights with real-time analytics can also act faster on potential business opportunities. A high throughput storage solution can support many users with massively concurrent connections and consistent performance at scale.

Agility helps keep up with changing data and applications. Workloads and operational requirements are constantly changing, in some cases hourly. Administrators are too busy with day-to-day routines, leaving them time to adapt storage systems to constant changes manually. Also, this process is anything but agile—and by no means the wise use of data architects’ precious time. A better approach is a cloud-optimized storage solution that supports variable data patterns and can scale with multiple workloads.

A cloud-optimized infrastructure is a must for running modern analytics applications. Organizations can enhance analytics capabilities with an on-premises storage foundation that offers the benefits of the cloud while operating on-premises. One of the critical features of a cloud-optimized storage solution is unified fast file and object Storage, available today as Unified Fast File and Object Storage, or UFFO for short. This enables analysis teams to achieve higher query performance and deliver insights faster.

Match data storage to usage model to keep costs down. Ideally, organizations only want to pay for the IT resources while scaling the storage platform up and down to meet ever-changing business needs. Subscription models for non-disruptive storage environment upgrades can help to stay up-to-date while keeping costs low.

Minimize the impact of outages and maximize uptime. The goal of data-driven companies is to be able to act on essential insights at lightning speed. As a result, they cannot afford planned or unplanned downtime for the storage solutions that support their advanced analytics applications.

It Depends On The Storage Solution

Data analytics offers excellent opportunities. With the right qualities in a data storage solution, organizations can empower more people—from developers to data scientists to generalists—with the modern tools they need to work with big data. The resulting insights are beneficial for making better and faster decisions.

Contemporary data platforms can help increase the maturity of data analytics and address the most demanding demands of modern data usage. The UFFO storage approach scores with cloud-like simplicity and agility while maintaining high performance and control over the data environment.

There are some challenges on the way to effective modern data use, but the technological barriers are rapidly falling. Today, when businesses are drowning in data overload, they can turn to storage solutions to meet the challenges. The possibilities are almost unlimited for modern, data-driven companies with more mature analytical skills and who are advancing confidently in this discipline.

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Security, Costs, Or Range: Radio technology is used if sensors are networked wirelessly for the Industry Internet of Things (IIoT). But not only the type of radio connection plays a role. We show you what to look out for.

The Internet of Things ( IoT ) and, in particular, the Industry Internet of Things ( IIoT ) lives not only from the fieldbuses to connect sensors and actuators. It is mainly sensors connected to the Internet of Things.

The traditional way to connect the sensors is via cables, the so-called fieldbuses. This makes sense and is preferable in industrial plants or environments prone to interference. Wireless technology is ideal wherever the sensors are difficult to reach or if the systems need to be connected over long distances.

It’s All A Question Of Radio Technology

Which wireless connection technology is used depends on various criteria. Entrepreneurs should ask this question when they use the right IoT connection. All systems have their respective advantages and disadvantages.

Whether low power wide area networks (LPWAN), classic mobile communications, or your building networks: the range of radio technologies on the market is diverse. It is always important to check the application for which the respective radio technology is to be used. Together with ECS, we have listed eight essential points that can help with the choice of wireless technology.

• Data: Based on the chosen use case, companies should ask how much data should be transferred, how quickly, and how often. Because the daily sign of life from a fire detector places completely different demands on a connection than the continuous monitoring of production systems or medical devices concerning a wide variety of parameters, such as temperature, noise, or vibrations, to be able to react as quickly as possible to problems, low latency times play a decisive role. Equally important can be the need for a bidirectional connection to transfer log files or software updates and individual device data.
• Range: The available radio protocols vary significantly regarding the maximum distance between transmitter and receiver. The required field depends crucially on the intended use. If there are many sensors within a manageable length, for example, in a factory or warehouse, mesh networks are an option. If a city or an industrial park is to be covered, long-range LPWAN networks are more suitable.
• Place Of Use: The location or position of use of the IoT devices is closely related to the range aspect. A transmission standard with high building penetration is required if the appliances are in underground garages or basements, such as narrowband IoT (NB-IoT). If, on the other hand, the sensors are mobile, such as when monitoring a vehicle fleet, this requires reliable wireless technology with high network coverage. This is where the classic mobile phone comes in handy.
• Energy Consumption: IoT devices and sensors often have to do without a fixed power supply. Many are battery-operated, mobile, or used in places difficult to access. For them to work for several years without changing the battery, it is, therefore, all the more critical that the selected radio protocol works as energy-efficiently as possible. This is precisely what LPWAN technology specially optimised for IoT scenarios, such as NB-IoT, LoRaWAN, Sigfox, or Misty, can do. In addition to their low energy consumption, they also shine with their long ranges. Reductions were made on the bandwidths. However, this is not important in scenarios with small amounts of data, such as data from a temperature, pressure, or frequency sensor, intelligent electricity meters, or networked garbage cans that only report when they need to be emptied.
• Availability: The best radio technology is of no use if it is not stable and scalable. It is, therefore, necessary to clarify how the network coverage looks in the planned area of ​​application and how market-ready the individual radio technologies are. 5G or NB-IoT, for example, are still under construction. LoRaWAN is also not available everywhere and is therefore ruled out in scenarios such as theft protection of construction machinery. Some frequency bands are only available in some world areas, while others can be used license-free across several continents. In the case of international use cases, such as container tracking, it must also be taken into account that specific radio standards are not permitted in some countries, so alternatives must be provided immediately.
• Future Security: The connection technology should be selected with foresight: How likely is the provider still existing in five, ten, or twenty years? What if the network operator shuts down older networks in favor of the next generation of mobile communications, as is currently the case with 3G? Can the preferred wireless technology grow with new devices, additional use cases, and new business models? Open standards, a large ecosystem, and a high degree of dissemination of the technology speak for future security. Because that usually leads to the long-term availability of hardware, software, and experts who can still provide support years later.
• Security: Wireless data transmission involves higher security risks than wired communication of classic fieldbuses. Therefore, companies should pay special attention to security mechanisms. Important aspects are the encryption method used, the options for authentication, and the integrity mechanisms offered to protect against data manipulation.
• Costs: Finally, it is essential to look closely at the total costs of the different radio technologies. In addition to the acquisition and installation costs for hardware modules, this also includes the ongoing operating expenses, including maintenance. A significant item here can be the network usage fees. Depending on the radio technology used, these can vary in height.

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In 2009, NoSQL introduced a new category of databases. They were highly specialised and initially seemed to herald the end of the dominance, of relational SQL databases, which had previously dominated the market almost unchallenged and lacked the specialisation capabilities to cover different needs and data types. But today, general-purpose platforms are making a comeback.

It is no longer just companies from the software industry that depend on the rapid development of innovative applications for the success of their business models. Whether airline, energy supplier, retailer, or fitness provider: A highly functional custom app is standard today and is expected by customers. Frequently used apps such as social media or streaming platforms define the expectation of responsiveness, optimization for mobile devices, preparation of desired content and information, security, personalization, and updates and insights in real-time. The “engine” behind these applications, which have long been part of our everyday lives, is data and their storage and processing in databases.

The forerunner of the Structured Query Language (SQL), on which relational data platforms are based, was already developed in the 1970s. SQL databases have long been the basis for data-driven applications. However, the increase in mobile devices and apps placed new demands on the agility of databases. The data collected for these applications is not limited to a limited number of attributes. The data sets have become increasingly unstructured and complex. The rigid structure of relational databases, consisting of rows and columns, does not solve this complexity and makes storing, analysing, and querying difficult.

The relational model reached its limits in the noughties. Ad hoc changes in the data structure to be stored or in their query patterns remained difficult. For example, architectures such as Large online retailers, with their massive amounts of data and their need for back-end data analytics, drove demand for a different approach.

Specialised Models: Competing With But Not Replacing SQL

In the wake of the NoSQL 2009 event, various new database types emerged: document databases, graph databases, columnar databases, in-memory databases, key-value stores, streaming platforms. All specialised solutions developed for specific use cases were initially only suitable for these. They all had particular advantages and disadvantages.

Relational databases, meanwhile, have not disappeared from the scene. Many companies still use them today, but their weaknesses slow down developers when it comes to querying, analysing, and effectively processing the ever-growing flood of data. Of the new models, document databases are the most common alternative today. Its popularity is due to its flexibility, allowing many data structures to be represented. Objects are stored as documents with possibly different attributes. With the help of deep learning, these attributes or “tags” increasingly enable pattern recognition, which speeds up the finding of results in queries.

Document Databases: Most Popular SQL Alternative Among Developers

Instead of the relational model’s rigid row and column structure, document databases map their internal representation more directly to objects in code, eliminating the additional layer of mapping required by relational databases. The benefit of this similarity is that since they are based on JSON-like documents, they are incredibly intuitive for developers to use. JSON is a language-independent and human-readable format that has become a widely used standard for storing and exchanging data, especially web applications.

A significant advantage of document databases is that the structure of the data, the so-called schema, does not have to be predefined in the database and that ad hoc changes in this schema are possible at any time. Such changes can occur for a variety of reasons:

• Source data is being delivered in a different format.
• Newly enhanced information is being collected.
• Recent queries are being sought to be supported.

Schema management is an essential part of working with relational databases, where the structure of the data must be pre-mapped to support queries about the relationship between different elements. The ability to support these changes without extensive database and application code rework increases the flexibility of developers.

Which Approach Does The Future Belong To?

The document database offers excellent potential for companies to use big data sensibly. Its vertical and horizontal scalability ensures that it “grows” with developing a business model and the growth in data volumes. However, relational databases are still common in web-based applications and online publishing. Specialised models are selected depending on the purpose and requirements. While it seemed that the future belonged to different, specialised databases, the trend is now reversing, as many companies are moving back towards the “generalist” models suitable for various use cases throughout the organisation.

There are multiple reasons for this:

• Users of specialised databases are pushing for feature enhancements, so they don’t have to switch between different data stores because they want to consolidate existing datasets across organisational and technological boundaries.
• Developers also want more integration. Nobody likes the relational structure as the only option back. However, instead of the effort involved in querying and analysing data within this structure, they are increasingly faced with the effort involved in integrating various specialised databases, each of which underlies specific applications. In extreme cases, this can mean that the higher integration effort nullifies the advantages of your agility in a particular area.

The Trend Towards Multi-Purpose Data Platform

Database providers have long recognized the trend reversal and are reacting: Their models are now much less highly specialised than in the early days of the NoSQL concept. MongoDB’s document database has provided data lake, charting, and other analytics capabilities since version 4.2 and time series capabilities since version 5.0 (2021). Different models are also expanding their solutions with new functions, thus moving more towards universal applicability.

This consolidation also has tangible economic reasons. The operating costs that arise from the maintenance of several specialised data stores are considerable and often lead to the emergence of operational silos that no company can use in the process of digitization: data that is stored in a particular format in a system must be converted, so they can be shared with other systems and the teams that use them.

Increased Safety Standards Are Easier To Meet

Data security and data protection are very high on companies’ agendas: the requirements for handling sensitive data have become increasingly strict in recent years, the increased number of mobile devices and access points due to remote work and the increasing shift of enormous amounts of data to the cloud has led to cyberattacks skyrocket with data theft. The higher the number of platforms, the more complex it is to meet compliance and security requirements. Each datastore must be secured separately. Its configuration must be checked for compliance with internal and external regulations. A multi-purpose data platform, on the other hand, provides a single point of control for security and compliance, in addition to operational simplification.

While specialised platform vendors add functionality to their databases to broaden their suitability for different use cases, general-purpose platform vendors add technical functionality simultaneously. This also increasingly eliminates the competitive advantage of specialised data storage and thus the need for complex maintenance. Modern application data platforms can support text search, time-series data processing, and analytics directly in the core platform without the need to integrate and set up data exchange with particular systems. While database vendors increasingly moved away from general-purpose workloads following NoSQL in 2009, the reversal has been evident over the past three years.

Conclusion

When selecting a single database, organisations should compare traditional relational data models and newer approaches. Instead, they should consider a comprehensive data platform whose underlying data model can support as many requirements as possible. The transformation in the database market is being driven by the demands of developers who need to meet the demands of end-users who want and need to derive business-relevant insights from their data.

The race winners will be data platforms that enable the delivery of desired functions. By definition, this goal requires a unified view of the data and various tools that can be used to analyse it. This is what modern application data platforms offer. So the future will likely belong to those solutions that combine the best of both worlds and can be used for a wide range of applications and provide specialised functions when required.

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