In the case of enterprise database administration, trends come and go now and then. A few decades ago, the concept of enterprise database management came up with relational databases, and overtime, it had grown from SQL to NoSQL and now NewSQL databases. We can now handle data of all structured and unstructured formats, not just storing those but also utilizing it for analytical and decision-making purposes.
As of late, we are also witnessing enormous data growth, which contributes to the concepts of big data and artificial intelligence, etc. Data is now streamed through various touchpoints like IoT, Big Data pipelines, mobile phones, POS, video records, etc., which all demand a better data management practice. Enterprise database users are constantly looking for more reliable and fresh data sources and trying to adopt a comprehensive approach to data management to the storage, manipulate, secure, and enable better governance with data.
In such a data-centric business administration model, all those who deal with the enterprise database management systems need to be diligent about the database best practices to ensure an optimum outcome. A Gartner study shows that there is a scope for more data disruptions in the future with the advancements in technologies like artificial intelligence, machine learning, the internet of things, deep learning, etc.
What to expect in 2021
Considering all these fundamental cultural shifts in terms of enterprise data management, we may expect the following changes in database management practices by 2021.
- Organizations may prefer to go for a decentralized data governance model, which can be a fundamental shift from the hierarchical architecture many follow now.
- Storage can be a multi-tier approach. Different entities may be used to specify different data types, and there will be a commonplace for data to optimize space usage.
- The primary database management activities as data stores, analytics, BI, etc., will be outsourced as DBaaS services, which will largely help to reduce cost and in-house effort in database management.
- The evolution of more and more cloud and hybrid public / private cloud databases services can be expected. These will ensure increased productivity and efficiency across enterprises.
- Database automation will take a big leap in 2021, and there will be a unique set of advanced technological tools to help simplify the activities of the fundamental database of storage, maintenance, provisioning, updating databases to advanced activities like analytics, and fine-tuning of the databases for the project workflow, etc. However, complete automation may still not be possible, and effective database administration always demands frequent human intervention.
A survey has shown that almost 63% of enterprises functioning worldwide have at least one Big Data applications running. As of late, database solutions offering cheap storage of huge data volumes, easy and anywhere data accessibility, and high-end automation features have paved the way to a cultural shift towards data-driven enterprise operations management. Big Data rules in the DBMS sector lately, which is largely used in biotech and trading applications.
As more enterprises ranging from SMEs to even the biggest Fortune 500 firms now wake up to a more insightful approach to data management, there is also a higher demand for live data streaming and quick processing of the same. Many such applications and tools that can facilitate these functions demand handling data in huge volumes, which is what Big Data fulfills.
Data streaming in big data
To understand this concept, you may consider a financial or banking application. Suppose there is a feature to calculate the EMI of a loan product or returns on a fund investment. Such applications need to take data from various updated sources and process tons of live details to provide apt results to the users. All these need to happen in a matter of milliseconds and the results to be shown. This is the process of data streaming. For ensuring better availability of databases, DBAs also need to consider approaches like database cloning.
So, data streaming in Big Data is about handling big data sets in a parallel way to offer the most accurate output more quickly. Based on data streaming’s effectiveness, applications may generate real-time info about what their potential users may look for the informed take decision based on the most recent facts. As the name data streaming implies, data is in constant motion here, which gets processed over different server clusters before getting stored. This data stream is sent in kilobyte sizes and processed per each record. The analytical tasks will also be run simultaneously, and for which, there may be tons of filtering, sampling, and aggregations are happening parallel. Speed is the primary element that decides the effectiveness of data streaming.
Data streaming and batch processing in Big Data
Another major feature to be considered for Big Data applications is batch processing, which can take a big chunk of data and run an in-depth analysis on it to give the most aggregated output. On the other hand, as we have seen above, data streaming considers comparatively smaller chunks of data, also known as micro-sets.
Considering the use cases, batch processing can be more ideal for an HR application while analyzing employee attrition rates in the organization or employee satisfaction. In this example, you can identify that each process’s datasets are considerably big and need to be processed for the whole inference. On the other hand, the data streaming approach can be ideal for HR activities like recruitment. The potential applications can be tested instantly to see whether they qualify.
However, the major common challenge in data streaming and batch processing is that these need to be accomplished in a matter of milliseconds for optimum results. Along with this, you may also find challenges in the case of scalability, fault tolerance, and security of data while dealing with this functionality of Big Data applications. So, considering your enterprise objectives, Big Data database administrators need to plan an effective batching methodology to choose between streaming, batch processing, or a combination of both to ensure optimum productivity.