Nine Reasons Big Data Projects Fail

As companies shift their Big Data to the cloud and hybrid environments, the need for Big Data analytics and a corresponding long-term analytics strategy has become increasingly critical. Here’s your opportunity to listen to experienced Big Data practitioners articulate their best practices in building successful, long term analytics architectures.

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.

Building performant ETL pipelines to address analytics requirements is hard as data volumes and variety grow at an explosive pace. With existing technologies, data engineers are challenged to deliver data pipelines to support the real-time insight business owners demand from their analytics. Databricks Delta is the next generation of evolution in big data processing from Databricks, the company founded by the original creators of Apache Spark.

Business users have a tremendous appetite for data. The “single version of the truth” was a rallying cry to deliver business data in data warehouses for years. Users were able to digest and analyze large volumes of corporate data. They reviewed trends, identified anomalies, and supported decision-making because they had the detailed data to support action. As the business/data environment matured, the need for more diverse detail and increased delivery speed only grew. The data lake became a successful mechanism for delivering data from diverse systems in a timely manner.