Operations | Monitoring | ITSM | DevOps | Cloud

This article was originally published in InfoWorld and is reposted here with permission. The compactor handles critical post-ingestion and pre-query workloads in the background on a separate server, enabling low latency for data ingestion and high performance for queries. The demand for high volumes of data has increased the need for databases that can handle both data ingestion and querying with the lowest possible latency (aka high performance).

Whenever we start a search consulting project from scratch, the obvious question is: which search engine to use? We’ve talked about Elasticsearch vs Solr before, but here we’ll compare Elasticsearch with its fork, OpenSearch. Chances are, if you need to decide between the two, you’ll be looking at a few dimensions.

Migrating an on-prem database to a public cloud comes with a number of benefits, such as no longer needing to manage and maintain physical infrastructure, dynamic scaling, disaster recovery, and overall cost reduction. However, migrating to the cloud can often be a complex and daunting task. For instance, if an organization is a Microsoft shop with teams that rely on SQL Server databases, Azure is a natural fit for its needs.

In industrial operations, time is money. The more efficient processes and machinery are, the better it is for business. Providing proactive monitoring and maintenance of industrial machines, however, is not an easy task. This is especially true as these machines become increasingly complex and distributed. It’s not possible to have maintenance crews on site for every asset in a distributed system. The edge is where the physical world meets the digital world.