Modern enterprises can collect enormous quantities of fast-moving and complex data that are necessary for making better business decisions and gaining a competitive edge. A significant portion of this data will come from Internet Of Things (IoT) devices. Experts are predicting that IoT devices and systems will generate over 500 zettabytes of data annually. With that said, cloud storage is currently the preferred option to store, process, as well as gather insights from the data. Read on to find out several notable cloud storage trends that are expected to actualize in 2019.
IoT Sensor Data Storage Updates
Amazon DynamoDB is considered to be a powerhouse for IoT data storage as it provides low-latency NoSQL databases for querying and storing device data. Boutique platforms like Losant and Ubidots, however, are delivering complete sets of app components and tools that allow end users to analyze, store, process, and connect their data both in the cloud and at the edge of cloud environments. Experts are expecting more businesses to adopt these types of dedicated IoT processing and storage solutions in 2019.
Cloud Storage Solutions Optimized for Machine Learning (ML)
Chances are, you might know about the enterprise AI revolution. While this is underway, more cloud providers are preparing to deliver their own AI infrastructure solutions to businesses. In addition, ML algorithms and learning models are driving an increasing number of enterprise AI use cases. Most of these models rely on loads of data to optimize performance, thus it will become more common to see cloud providers further optimizing and refining their cloud storage options with machine learning models.
Migrating Dark Data
Dark data refers to information sets that businesses collect but failed to maximize them. Oftentimes, these data sets contain value. The current issue is that they are stored in unstructured formats and are not accessible via normal queries. With that in mind, extracting dark data will become more widespread this year, especially as businesses attempt to improve their analytics by accessing larger data pools. In these cases, cloud storage solutions come in handy as they can help lower the cost of the overall extraction project.
Big Data Storage Tiering Services
Whether enterprises utilize an on-premises infrastructure or a dedicated private cloud infrastructure to store big data, public cloud storage services still play a role in tiered storage. Storage tiering services typically apply policy-based data classification rules to transfer data between various types of storage technologies. In addition, they can add an extra level of cost efficiency, which is great for businesses that are looking for low-cost options to store occasionally accessed data.
Big Data Analytics Supported in Cloud Environments
More companies are looking for data storage solutions that can meet the high demands of supporting big data analytics. In 2019, there may be a rise of high-performance distributed cloud storage options. Microsoft and Google are some of the cloud storage providers that are recognizing the increased need for more powerful storage solutions, i.e., enhancing their existing services to conduct big data analytics at various performance levels. Other cloud storage providers will certainly try to catch up by offering solutions that can support workloads associated with big data analytics.