DATA MANAGEMENT

DataOps integrates principles from Agile methodologies to manage data workflows efficiently and to improvement across the full data lifecycle

Components of modern database management includes 1. Hardware 2. Software 3. Data and data store 4. Query processor 5. Storage engine etc.

AWS data management is achieved through various managed services like AWS Glue that support batch, real-time, and hybrid processing models.

AI Data Management is the application of AI, ML & automation to improve collection, processing, storage, retrieval, & governance of data.

Major google data management tools are 1. Cloud data fusion 2. Google dataflow 3. Bigquery data transfer service 5. Google pub/sub, GCS.. Read on for more.

Are you noticing bottlenecks or disadvantageous data silos in your enterprise workflows? Enterprise automation uses software programs and other technologies to perform repetitious business processes, and it could be the...

Organizations succeed in their data journeys when they ensure they develop a strong partnership with their solution providers. Solution providers need to be committed to support and ensuring implementation success...

Businesses need to understand data governance and make sure to prioritize initiatives as their data management environments mature. Complexities increase as more data pipelines are created. Data governance can help...

Data mesh helps organizations look at data projects from a business perspective and manage their outcomes more effectively. Data pipelines take into account business domains, enabling better data governance planning.