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.
#1 Management of data is still the hardest part
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.
#2 Data Silos are continuously proliferating
This is not, however, a much difficult prediction considering the evolvement of data structures over time. During the time of Hadoop boom a few years ago, we embraced the idea of consolidating all data for both transactional workloads and analytics on to a single platform. In this way, data gets stored in silos.
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.