A smiling man in a suit uses a tablet in an office, illustrating the concept of orchestration in data management.

In computing, the term Orchestration describes allocating and coordinating resources and data to achieve a goal, such as moving and transforming raw data into an analyzable form. This form of orchestration is known as a data pipeline.

In IT operations and cloud computing, orchestration can be used to describe the infrastructure setup to support application testing and execution.

Why is Orchestration Important?

Orchestration refers to the automation and sequencing of a series of operations to complete a business and gain operational efficiency. Performing such tasks manually is more error-prone. Early computing systems relied on operators to provision hardware, load data and launch software to support applications. This could take hours. Today, virtual machines, containerization, and data integration technology can create applications and data platforms on demand and reliably. Data pipelines can automatically extract, transform and load (ETL) data into a destination data repository as it is created to provide real-time analytics.

An Example of Orchestration Steps for a Data Pipeline

Data pipelines provide end-to-end visibility and control over the flow of data. Below are some of the typical steps in a data pipeline flow:

Data Connection

Raw operational data is gathered from multiple data sources, such as transactional systems, log data files, and sales and marketing systems. Data integration technology provides application programming interfaces (APIs) and software drivers that connect to varied data sources.

Data Profiling

Profiling datasets provides statistics about the data, including data volumes, cardinality, data types, averages, totals, and variance values.

Data Extraction

Structured and semi-structured files can be record-based or document-based. Data can be extracted into JSON or XML formats for API-based downstream access.

Data Preparation

In this step, data can be sorted, irrelevant data can be filtered out, and gaps can be filled. Field formats can be made uniform for more effective query processing.

Merging Datasets

Orchestration becomes more worthwhile when multiple dataflows need to be merged, especially if the data merge is conditional and dictated by a rule’s engine.


The final step of a data pipeline process is a data load. This can be as simple as creating and populating a single data warehouse table or as complicated as creating a partitioned object that has to support parallel access due to its excessive size.

IT Operations

Orchestration solutions can test and deploy applications on software-defined infrastructure. This is especially useful for supporting development QA testing and DevOps functions. Component-based applications depend on orchestration tools to rapidly provision containerized cloud-based application functions that need to support dynamic user loads.

Parallel Orchestration

Time-critical operations often need to be accelerated by splitting a task into multiple parallel subtasks run concurrently to process a subset of data that is eventually combined. Clustered systems and multi-core servers provide the hardware to enable parallel operations. These systems need software such as Apache Hadoop to provide the necessary data partitioning and coordination of subtasks to allow efficient parallel processing. Using cloud hyperscalers is also an efficient method of accommodating parallel orchestration as it provides the elasticity to scale.

Benefits of Orchestration Software

These are some of the benefits of using orchestration software:

  • Provides reliable repeatability for process automation tasks.
  • It allows reuse of operational functions across workflows, speeding up new flow development and reducing development costs.
  • Automation provides efficiency and reduces the risk of manual operations.
  • Consistency results in reliability.
  • Management costs can be reduced as operators can focus on exceptions rather than running operational orchestration tasks.

Orchestration in the Actian Data Platform

The Actian Data Platform makes it easy to automate data pipelines to store and analyze data across on-premise and cloud platforms. By combining class-leading data warehouse technology with a comprehensive data integration solution, operational data can contribute to business insights as soon as it is created.

Vector is a columnar analytic database that accelerates queries using chip-level parallel query and cache technology on any server. The Actian Data Platform has its query manager and visualization capabilities and connects to sophisticated business intelligence (BI) solutions that provide more advanced analytics and dashboards.

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