Over the years, the data landscape has changed. What you can do with data today has changed considerably and with that expectations have escalated about what is now possible. At the same time, the cost of storage has fallen dramatically, while the number of ways in which data is collected keeps on multiplying.
Not all data is the same. Some data arrives at a rapid pace, constantly demanding to be collected and observed. Other data arrives at slower rates, but in very large chunks. You might be facing an advanced analytics problem, or one that requires machine learning. These are the challenges that big data architectures seek to solve.
With A big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The threshold at which organisations enter into the big data realm differs, depending on the capabilities of the users and their tools. For some, it can mean hundreds of gigabytes of data, while for others it means hundreds of terabytes. As tools for working with big data sets advance, so does the meaning of big data. More often, this term relates to the value you can extract from your data sets through advanced analytics, rather than strictly the size of that data.
The evolving data architecture challenge
The cloud is rapidly changing the way applications are designed and how data is processed and stored. The choice around which is the best architecture for a particular organisation’s goals is not straightforward and requires a full understanding of data and business requirements as well as a thorough knowledge of emerging technologies and best practice.
Traditional vs big data solutions
Data in traditional database systems is typically relational data with a pre-defined schema and a set of constraints to maintain referential integrity. Often, data from multiple sources in the organisation may be consolidated into a modern data warehouse, using an ETL process to move and transform the source data.
By contrast, a big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The data may be processed in batch or in real time. These two categories are not mutually exclusive, and there is overlap between them.
The skill is in selecting the relevant Azure services and the appropriate architecture for each scenario. In addition, there is also work to be done on the technology choices for data solutions in Azure which can include open source options.
How can Adatis help?
Working with each individual client using key selection criteria and a capability matrix, the data architects at Adatis can help choose the right technology for any particular circumstances.
The goal is to help you select the right data architecture for your scenario, and then select the Azure services and technologies that are the best fit with those requirements.
Here the Adatis team share their latest perspectives on all things advanced data analytics