Data pipelines are typically used to collect, ingest and process data with technologies including Apache Hadoop, Spark, Scoop, Pig, MapRe, etc. They provide the ability to programmatically create highly complex workflows often defined as code as part of the CI/CD development lifecycle.
ORCHESTRATING DATA PIPELINES
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Summary & benefits
- Ability to execute fast and accurate big data implementations by replacing manual scripting with automated workflow orchestration.
- Less time spent building connectors and coordinating multiple tools
- Shortened development time, prevent coding errors to build manageable, changeable data pipeline workflows at scale.
Every day companies ingest vast amounts of data from various sources, such as transactional systems of record, operational databases, logs, applications, sensors and devices. To extract the actionable intelligence your business needs, this data must be synchronised, ingested, consolidated, cleansed, and analysed. Orchestrating workflows is typically done with multiple disconnected scripts, which are time-consuming to develop, maintain and scale in production: they also change critically when inevitably the data environment itself develops. This complexity limits the speed at which data applications can be delivered, making it difficult to adapt to the challenges posed by accelerated applications, disparate data, and increasingly diverse infrastructure.
As part of our managed service, we can help you orchestrate and automate complex data pipelines, at scale, across hybrid environments and leading technologies to ensure consistent service levels.
You can orchestrate data workflows on premise and in the Cloud, while managing complex workflow dependencies during data ingestion and processing in many modern data platforms such as Amazon EMR®, Azure® Data Factory, Google Big Query, Spark, Hadoop, and Snowflake.
This results in an end-to-end view of data pipelines at every stage, from ingestion, and processing through to storage and analysis.