What is a job scheduler API?

A job scheduler API is a programming interface that allows developers to automate and control the scheduling of jobs within a software environment. This type of API enables the creation, management and deletion of jobs. Through endpoints exposed by the API, users can specify job details such as job name, runtime and the specific automation schema in the request body. Functions typically include running jobs at predetermined times or in response to specified events, managing job retries and overseeing job execution statuses. Job scheduler APIs are essential in environments like Google Cloud, where managing and scaling numerous tasks efficiently is crucial. Finding the right process automation tool for IT.

What is an API scheduler?

An API scheduler refers to a tool or service that organizes and controls the timing and order of API calls within software applications or between services. It helps to efficiently manage the execution of multiple API requests by scheduling them based on predetermined conditions or dynamic requirements. This tool is especially useful for handling dependencies among tasks and making sure API calls occur at the right moment to maintain system integrity and performance. Features of an API scheduler include webhook and callback setup, permissions handling and authentication with API keys to secure communication through headers. Discover how to rapidly create APIs for reliable end-to-end processes, without custom scripting so your team can achieve more, faster.

What is the API specification of a task scheduler?

The API specification of a task scheduler outlines the technical details and protocol for interacting with the task scheduling service. This specification includes definitions of available endpoints, supported API calls, data structures and...

ActiveBatch Big Data/Hadoop Ecosystem Integrations

ActiveBatch Extension for the Apache Hadoop Ecosystem

Simplifying Workload Automation and Enterprise Job Scheduling for the Hadoop Ecosystem

It isn’t only the bigness of the data, the volume, that creates difficulties, but the number of disparate systems that contribute data and the cumbersome scripting required to handle and analyze the data.

Big Data engineers are looking for a workload automation and enterprise job scheduling solution that simplifies the integration and ongoing maintenance of the many Big Data and Hadoop components within the IT Infrastructure to maximize results. Once the Hadoop data sets (email messaging, documents, videos, audio files, presentations, telemetric sources, and more) are collected from structured and unstructured sources throughout the Big Data Ecosystem, the focus turns to preparing and organizing the data for use by a Big Data analytics team. The goal of that team becomes identifying trends and opportunities faster to allow the enterprise to address the challenges of a changing world.

Spend Less Time Preparing Big Data and More Time Visualizing It

ActiveBatch is a workload automation and enterprise job scheduling solution that simplifies the development and ongoing maintenance of processes through a unique templated Job Step approach for the Hadoop Ecosystem. This approach has been proven to simplify IT workflow creation. ActiveBatch Workload Automation runs within the framework of a Hadoop grid or cluster from prominent distributors such as Cloudera, MapR, Hortonworks, Amazon, and others.

Big Data and Hadoop Automation Benefits

Reduces both the time and cost spent building and maintaining the repetitive assimilation of Big Data
Minimizes the risk of manual errors by decreasing dependence on custom script creation
Optimizes the efficiency and speed of business and IT workloads to deliver faster time to insight by business users
Eliminates wait time for Job execution with an HDFS File Trigger to instantiate workloads beyond interval, date and time, or constraints

Apache Hadoop: The ActiveBatch Extension

The ActiveBatch Integrated Jobs Library allows developers to easily assemble IT and business workflows while improving reliability and reducing the time spent to modify and maintain those workflows. ASCI engineers have researched, designed, and engineered the logic, so developers won’t have to, in our pre-defined Job Steps that support the Hadoop Ecosystem and its major components and their subsets.

Hadoop Ecosystem Subsets Supported by ActiveBatch

Pig HBase Sqoop Spark	Hive HDFS MapReduce Oozie

The content-rich ActiveBatch Integrated Jobs Library includes prebuilt logic in the form of drag-and-drop Job Steps, supporting important functions such as:

Managed File Transfer (MFT) Loading data Generating reports	Running scripts Flow control And more…

What ActiveBatch Users are Saying

“If it’s digital, ActiveBatch can do it.”

—System Administrator, First Rate

ActiveBatch Big Data / Hadoop Ecosystem Integrations

Simplifying automation for the Hadoop Ecosystem with ActiveBatch Extensions for Hadoop tools and platforms