Choose the right data management technology for each of your unique workloads
Using a sub-optimal solution for your data management workloads can result in slow and missed insights. Offerings that can handle several workloads exist, but as IDC’s recent report notes, often lack the performance of workload-specific or data-type-specific options like streaming data capture systems and data warehouses.
With much of the data landscape looking the same, IDC’s report dives into which data types, uses, and technologies fit together best, examining vendor options from IBM, Oracle, Microsoft, and many others in detail. A few examples are below:
- If the data requires a schema and agile development is important, a NoSQL document system or key-value store may be ideal
- RDBMs can be classified as transactional, analytic-transactional, analytical, and deep analytical. Uses and capabilities vary such as the first two handling ACID transactions.
- Data science thrives with curated data in Hadoop (HDFS) or object storage with Spark, Presto, and similar technologies.
Read the rest of IDC’s report on workloads from Data Science to edge analytics and beyond, so you’ll be better prepared to choose the solutions that best fit your needs.