Database Trends in 2019 – Data Governance and Real-Time Data Streaming
More data is pumping in for good reasons, and all types of business organizations now have unlimited data sources to be tapped. Data is the most valuable commodity in the modern world undergoing a digital revolution.
However, unlike the old-time concept of collection and classification of data, now the requirement is to turn this huge collection of data into actionable business insights. This remains the biggest challenge of all times for the DBAs and business administrators who deal with data. The organizations which find better solutions in terms of formidable data challenges may enjoy success over others in the highly competitive market.
Keeping these primary considerations in mind, let’s explore the top trends in big data which the organizations could look forward to in 2019.
It has been so forever, and in times of big data, it has become more obvious. Finding the most interesting patterns and insights hidden in a huge volume of data is the challenging part. Machine learning is now succeeding up to an extent in spotting those unique patterns accurately and then derives some actionable insights by acting upon them.
However, putting this into production is a lot harder than how one defines it. For the beginners, amassing the data from different sources is a fairly difficult task which requires an excellent database and ETL skill. Cleansing data and labeling it to train for machine learning will also take a lot of effort and time, particularly when deep learning is used. Ultimately, putting such a system into production at a scale in a reliable manner also requires a unique set of skills.
For all these obvious reasons, data management remains the biggest challenge for data engineers, which will continue to be one among the sought-after skill in the big data times. In addition, big data is used when building a marketplace Amazon level.
This idea was; however, never panned out widely for many reasons. The biggest challenge was that various data types as relational DBs, graph, DBs, time-series database, HDFS, etc. might have various storage requirements. The developers will not be able to maximize their potential if they have to cram all the data into a one-size-fits-all type of Data Lake.
In many cases, amassing a huge volume of data into a single place make sense. The cloud data stores, for example, are offering organizations a flexible and cost-effective unlimited storage, whereas Hadoop remains cost-effective storage of unstructured data with analytical capabilities. But for many other organizations, these remain as additional silos which have to be managed. They are important and big silos, but not the only ones. If a solid centralize force is not in place, then the silos will continuously proliferate.
Some new technologies like NewSQL database and in-memory data grid enable streaming analytics up to an extent and also converging around the common capabilities. All these are expected to contribute effectively towards ultra-fast live of processing of the incoming data by using machine learning and deep learning models. It will also contribute further to automated business decision making too. Combined with the current SQL capabilities at the open source frameworks like Spark, Kafka, and Flink, real-time analytics had a better scope in 2019.
Data breaches are now on top of the talks and according to a survey conducted by The Harris Poll, about 60 million US citizens were adversely affected by the identity theft last year. This is a 300% increase compared to the previous year of 2017, at which time only about 15 million were affected. More and more organizations now realize the fact that the Wild West days of big data are slowly sinking. Even though the US Government hasn't yet started penalizing your for being reckless in data administration, it will surely show up anytime sooner.
When it comes to any technological change, it is the human resources which remain as the biggest cost. This is the same in the case of big data projects too. Even though automation and technological innovations help to reduce human work volume, ultimately skilled people are those who build the structures and run it to make things work. So, the primary goal is to find the right person with the right skills to turn data into actionable insight irrespective of the technologies and approaches used for it.
However, in line with the technological advancements, the skill mix of database administrators also changes. In 2019, we can expect a huge demand for those who put in a neural network in production. In terms of the technical skills in demand for data scientists and machine learning experts, Python continues to have a higher demand in terms programming platforms along with R, Mat lab, SAS, Java, Scala, and C, etc. Using these languages, you can make a sports betting website from scratch.
As new data governance strategies and programs changing into top gears, we can see an increased demand for data stewards in 2019. Data engineers and administrators who can work with the core DBA tools like Spark and Airflow etc. will find an increasing number of opportunities for them. You can also see demand for the machine learning experts accelerating in the next couple of years.
As above, we can expect that in 2019, progress in terms of big data management will emerge from a multitude of fronts. Even though there are substantial hurdles in terms of technology, legalities, and ethics raised by big data and machine learning, the potential benefits of these technologies will far outweigh the risks and drawbacks.
#1. #1 Management of data is still the hardest part
#2. #2 Data Silos are continuously proliferating
#3. #3 Real-time streaming of analytics
#4. #4 Data governance
#5. #5 Skills shift in data administration