Data and data everywhere! When it comes to business database administration, the major challenge for modern day enterprises is to handle the huge volume of data pumping in from every touchpoints and sensor. Those organizations finding more mighty solutions to handle this gigantic volume of data for good tend to succeed in the highly competitive market. Keeping this requirement in mind, let us first have an overview of data governance trends in 2021.
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Data management is getting more and more challenging
DBA’s face a huge challenge in terms of effectively managing big data. This has become more obvious lately. Finding the relations between data and deriving insights from it has become very interesting lately. Big data and machine learning succeed to a big extent in deriving data relations and patterns most accurately and providing actionable insights to the business decision-makers.
However, based on different industries’ varying nature, it has become really difficult to define a structured pattern for universal database management. For those who are new into it, amassing the data from various sources need high-end technological and ETL skills. Data cleansing and organization for machine learning capabilities also take a lot of time and effort, especially when deep learning technology is involved. Overall, putting in an appropriate system to handle data at scale requires the most advanced skills and strategies.
Data structures are unprecedently proliferating
Data silos are not predictable nowadays by considering the continuous unprecedented evolvement of data structures. A few years ago, when Hadoop came into the picture, we started with the plan of consolidating both analytical data and transactional data into a single platform. However, this idea was not widely accepted for many good reasons. A major challenge in data consolidation was that there were different types of relational databases, time-series DBs, graph database, HDFS and so on. Each of these has different storage requirements too. DBMS developers could not optimize the potential of the DBs to manage all these data into a single data lake.
In some cases, consolidating all the huge volumes of data into a single DB is sensible. Cloud data management stores offer enterprises a highly cost-effective and flexible solution for huge storage. In contrast, solutions like Hadoop remain very cost-effective for unstructured data management for analytical purposes. On the other hand, for many DBMS users, managing data will remain just like some additional silos that need to be handled. These too are important for the big silos; however, not the only choices to make. If there is no central force in place, these silos may proliferate continuously. So make sure that you have everything in place to get the best out of it.
Real time data streaming for analytical purpose
In the modern era of big data, the basic objective for data management for decision makers is to get insights from every piece of data handled. Data is now the major driving force behind analytics and business decision making. However, the major challenge is the need to pull out the neediest pieces of data based on the priorities for the analysis. This purpose has to be considered from the very beginning of database design, for which you can take the assistance of consultants like RemoteDBA.com.
Some of the latest database management technologies like NoSQL and NewSQL DBs offer in-memory data grids, enabling analytics during live data streaming with increased accuracy. All the changing models are expected to improvise the new-age databases’ efficiency and deep learning models on data. These may also contribute to automated decision making in business management.
SQL vs. NoSQL as business databases
Considering all the above fundamental shifts in enterprise database management, the businesses need to choose an appropriate database for their purpose. Adoption of an ideal real-time business database system is a critical decision in terms of organizational success. Along with the conventional choice of relational DBMS or SQL databases, you now can choose non-relational or NoSQL databases. As of late, there is a third choice also a NewSQL, which is, however in its initial stage of development.
Both SQL and SQL have their own merits and drawbacks. Each organization need to consider these in light of their functional requirements and business objectives to choose the appropriate DB. Further, we will discuss some distinctions of SQL vs. NoSQL.
SQL vs. NoSQL
There are many differences between these databases. The business users need to understand the differences between these to choose from SQL and NoSQL as enterprise DB. Let us further discuss a few fundamental differences to be considered.
The primary difference between SQL and NoSQL in the DB language. SQL users structured query language for data management. For decades now, SQL is highly accepted and used widely as a database language. However, SQL is restrictive, too, in some aspects. SQL sticks to some defined data schemas for the database structure, which is restrictive in the time of big data. You need to ensure the defined formats of data inputs which need good preparation and careful consideration of database setup.
However, NoSQL follows a more dynamic data scheme that can also handle unstructured data stored in various formats as column-based, graph-based, document-based, and as key-value stores. These flexible modes of data storage will let the users customize the documents easily without any pre-defined data structure. Users can easily add the fields and syntax may also vary in different databases without creating any problems.
Another major difference to consider between SQL and NoSQL DBs is the database structure. SQL needs the DBs to be strictly table-based. So, SQL may be an ideal choice for the enterprise applications which need row-based transactions. You can find examples of such structure in accounting systems or other systems built on relational models. On the other hand, NoSQL tends to be in key-value pairs which follow wide-column stores.
You can find other major differences between SQL vs. NoSQL are scalability, flexibility, capacity, etc. All needed to be considered based on your database objectives to choose DB for your business.
