6 Steps for a Successful Data Migration

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.

The pace at which the field of data science is expanding makes it impossible for the enterprises to catch up to the state of the art solutions. To avoid falling short of the demands of burgeoning digital revolutions, solutions like APIs, drag and drop analytics, low code and even no-code have been developed. For instance, no-code tools bridge the gap between domain expertise and niche skills of the developers. Know more about why enterprises are increasingly leaning on no code solutions in the upcoming webinar being organised by Analytics India Magazine in association with HP.

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Fresh data beats stale data for machine learning applications. This on demand webinar discusses the value of fresh data as well as different types of architecture and challenges of online prediction, it will also cover the tradeoffs between latency, staleness, and cost.