Around some 16 years ago, when I was pursuing my bachelors, we were taught about Relational Database Management Systems (RDBMS). The databases being used in universities then were mainly My SQL or Oracle. Those were the days when businesses were concerned just about Normalized tables or ensuring Primary key-Foreign Key relationships between entities in RDBMS. There were other not so popular databases as well in the market, such as MS Access and MS Excel. Life was simpler with very few databases around!
It is said that ‘necessity is the mother of invention.’ Something akin to this has been happening in the world of Information Technology. Mobiles, E-commerce, Social media and Internet, all contributing towards the birth of different types of databases. As the data kept on increasing at a faster rate thus did the need of bigdata storage and processing. As time flew, slowly and steadily, we were surrounded by not only RDBMS, but also NoSQL databases, Cloud, Hadoop storage, and other processing technologies. By the end of the first decade of the century, we already had started addressing terms like SaaS, PaaS, and bigdata more often.
As businesses and technologies keep on evolving, so does Pitney Bowes Software. Since we have been the pioneers in the in fields of data quality and address quality for more than two decades, we have witnessed and observed the evolution of data very closely. We understand the needs of all sort of businesses, ranging from small to large enterprises. Earlier, there were RDBMS and flat files, but nowadays, for every business requirement, there is a separate database available in the market. For example, small-scale organizations might like to keep their data in the cloud rather than maintaining in-house. The choice of database completely depends on the type of use case any department or organization is trying to achieve.
In the past decade, we have evolved ourselves from traditional data integration tools to data federation software. We have the capability to connect to Hadoop ecosystem such as HDFS, Sequence Files, Hive, and HBase, NoSQL databases such as Mongo, Couchbase, and Cassandra, or Cloud files systems such as AWS S3, Azure, and Google storage. With so many databases coming into existence, it becomes the responsibility of leaders like us to keep ourselves up to date with the market needs.
With the growth of data, there are different challenges that businesses have to resolve. Either organizations are planning to migrate to bigdata sources or they have already migrated their business. In addition, with different departments storing data in different sources, it becomes inevitable for any organization to keep them in accord with each other. The competitiveness among companies in their respective fields has forced them to make strategical decisions. With Mergers & Acquisitions (M&A) coming into picture, the whole task becomes cumbersome and data gets error prone.
These few use cases, along with many others, are when our experience comes to the rescue. In the current era, the capability of keeping data clean, maintaining a single source of truth, or performing data analytics on cleaned data can only be complimented when the tool can also ingest data from variety of sources. That is where Spectrum’s Big Data Integration module can be easily fit in.
So what is your strategical goal on storing data? Are you also struggling to improve and maintain data quality? Do you want to migrate your data from RDBMS to bigdata sources? Do you want to perform data and address quality operations natively on Hadoop, Spark, and Hive?
If your answer is yes for any of the questions, feel free to comment, start discussion thread or go through the link here, we would love to hear!