What’s new from Elastic for DevOps and Big Data?
Elastic is popular for its open-source search experiences among users in the fields of database and Big Data. It aims at deriving actionable insights from the large pool of data and has gained widespread popularity owing to its reliability, transparency, and ease-of-use. Elasticsearch is one of the most popular services offered by Elastic. Elasticsearch can be integrated with Hadoop using the ES-Hadoop connect, a two-way connector that will enhance the power of insights drawn from big data.
Elastic has come up with some amazing releases recently, considering the present market scenario and analyzing the requirements of software users in the technology landscape. All these upgrades are aimed toward enhanced performance. Here’s a look at the new releases with which you can gain productive insights that could help you propel your business to the next level.
On 1st of April 2019, Elastic announced the new Elasticsearch JavaScript client (RC1). This JavaScript client was developed with an aim to overcome versioning problems faced by the old JavaScript client. It will eliminate the need to re-publish the older versions and time & efforts spent on searching scoped packages. Elastic used package reorganization in which a new package having a new codebase and a correct version number is published. This causes the current un-scoped package to be dependent upon a scoped module which, in turn, points toward the latest version.
Elastic Infrastructure’s 7.0 edition was released on 10th of April 2019 which tracks and monitors KPIs of the entire application stack. It is also capable of finding and fixing flaws and deficiencies in the system. This new release is an integration of four new modules which enhance the system’s observability and visibility. It consists of the NATS module, which collects and stores performance metrics. It also includes the Open Source Message System used in Kubernetes and cloud deployments. The SQL Server Module (beta) incorporating log transition metrics and AWE EC2 has direct access to instances in the cloud which it utilizes to collect utilization resources.
It also performs the important task of monitoring Prometheus time series metrics and alerts the user about undesired fluctuations occurring in it. In case of Kubernetes-based servers and other services which are dependent on such servers, root-cause analysis becomes extremely difficult due to the complicated nature of such systems.
The ECS (Elastic Common Schema) is designed to establish uniformity while transforming a conceptual data model into a logical one for it to be compatible with the database. This improves the efficiency of resource analysis and helps in obtaining meaningful insights from such data. It also simplifies and facilitates the wide variety of operations performed on databases including search, compare, and correlate, among others.
On the same day, Elastic released Elasticsearch for Apache Hadoop 7.0.0 with an aim to eliminate the need for cascading and further slicing of partitions. Getting rid of cascading further avoids additional costs involved in testing new features that are added to the suite as the technology upgrades. It is important to note that this release will need Java 8 or a higher version to run efficiently.
Logstash 7.0.0 released on the same day comes with a plethora of plugins, facilitating backward compatibility. It ensures ease of upgrading without the need to …read more
