Systems | Development | Analytics | API | Testing

Let's get started with this great debate. First, a step back; we’ve pointed out that Apache Spark and Hadoop MapReduce are two different Big Data beasts. The former is a high-performance in-memory data-processing framework, and the latter is a mature batch-processing platform for the petabyte scale. We also know that Apache Hive and HBase are two very different tools with similar functions. Hive is a SQL-like engine that runs MapReduce jobs, while HBase is a NoSQL key/value database on Hadoop.

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform.

The era of manual data management and governance is rapidly coming to a close. The size of the trove of data at nearly every company has become so enormous that it cannot be maintained using manual cleaning, cataloging, governance and search methods. The release of Lumada Data Catalog 6.1 breaks new ground in automating data management, cleaning and governance processes, making it easier to find data and grant access to those who need it.

French multinational automotive manufacturer Renault Group has been investing in Industry 4.0 since the early days. A primary objective of this transformation has been to leverage manufacturing and industrial equipment data through a robust and scalable platform. Renault designed an industrial data acquisition layer and connected it to Google Cloud, using optimized big data products and services that together form Renault's Industrial Data Platform.

Last week, we shared information on BigQuery APIs and how to use them, along with another blog on workload management best practices. This blog focuses on effectively monitoring BigQuery usage and related metrics to operationalize workload management we discussed so far.

In our modern world, accelerating the process of extracting insights from data is a complex challenge. Exacerbating this task are colossal data volumes, the expansion and use of multiple cloud platforms, and the increasing demands for self-service in a way that maintains compliance. Enterprises attempting to tackle the problem encounter various forms of friction everywhere they turn.