Data orchestration refers to the process of coordinating and managing the flow of data to ensure seamless interaction and integration between different data sources and systems. Effective data orchestration enhances data accessibility, quality, and consistency across the entire data ecosystem.
Why is Data Orchestration Important?
In the early days of IT, systems programmers would write utilities to automate tasks that operators in the machine room often did manually. These included mounting magnetic tape spools, responding to operator console prompts and starting applications. Over time, automation software has allowed IT departments to scale by eliminating the need for human manual intervention.
Operating systems now run startup scripts to prepare IT environments to host applications. Virtual machines can emulate hardware, and containers have made virtual machines portable across cloud platforms, operating systems, and hardware. Orchestration software can string together multiple tasks and schedule activities, so humans only need to worry about failures and exceptions. This allows IT departments to keep pace with the rapid growth in the volume and complexity of applications. As applications evolve to be more component-based, their number will continue to grow, and the need to manage their infrastructure will become even more critical.
Data warehousing relies on disparate data from internal operational systems and external feeds from web analytics and social media. Getting the data warehouse populated with clean data requires a multi-step process. Orchestration tools help to organize and schedule the data pipeline that encompasses the ETL (Extraction, Transformation and Loading) process.
Data Orchestration Tasks for a Data Warehousing Application
The following is a selection of tasks that must be orchestrated in a data warehousing workflow. DataConnect is a data integration solution that provides tools to visually construct a data orchestration workflow, such as the example below.
Data Profiling Tasks
Profiling source data sets involves scanning data to understand its size, variability, structure, and content. Subtasks can include identifying duplicate records, grouping data values into ranges, and pre-calculating Min, Max, Mean, Median, Mode, Standard Deviation, Sum, and Variance statistics.
Cleansing Data Step
Data profiling will help identify field delimiters, which the data cleansing data process will use to make data fields and records consistent by standardizing data types and file formats.
Filtering Step
Outlying values and unnecessary data can be removed to avoid skewing analysis results.
Transforming Data Step
Data often needs transformation to fix various issues, including inconsistent date fields such as numeric fields containing currency symbols and numeric values expressed with a different number of decimal places. Data transformation can correct these inconsistencies. Leading or trailing spaces can be made uniform. Sensitive data can be masked or obfuscated to protect customer privacy.
Data Augmentation Step
Data sets can be enriched by adding calculated values and merging related data from multiple sources. Gaps can also be filled by adding default values, extrapolating, or interpolating field values. Data from internal systems can be combined with external third-party data to provide a market context.
Partitioning Machine Learning Data
When datasets are too large to be read by a single process, they can be partitioned into sub-sets and placed on different devices for faster ingestion through parallel execution. Data can be partitioned using a high cardinality key value range or hashing values for a random, even distribution of records.
Data Validation Step
Data validation is the final step before the orchestration process uploads the data into the data warehouse.
Data Loading Step
Data loading can be done as a single thread for smaller volumes and parallel threads for large database objects. The parallel load process is itself one of orchestration in which a master process subdivides the work across multiple processes or workers, each loading a subset of the source data.
Orchestration Tasks for Application Deployment
The goal of IT service or application deployment is to reduce the incidence of errors. Modern application development uses continuous integration and deployment (CD/CI) processes that ensure tested software releases are deployed with confidence. Agile development methodologies deploy smaller incremental frequently.
Orchestration software uses a series of scripts to provision servers as virtual hardware images in the cloud or on-premise. Preconfigured operating system images are recovered from validated copies to the virtual servers. Support services such as web application servers are started before the application is launched.
Developers have the option to use container services such as Google GKE, which can be used to rapidly provision running services that have been packaged with all the IT resources they need.
The Benefits of Data Orchestration
Some of the benefits of orchestration include:
- More reliable IT and data pipeline services thanks to automation.
- Exception-based management makes efficient use of limited IT resources.
- Eases the creation of new orchestration processes using existing components.
Actian and Orchestration
The Actian Data Platform makes it easy to orchestrate data preprocessing thanks to its built-in data integration capabilities. Organizations can get full value from their available data assets because the Actian platform makes it easy to unify, transform, and orchestrate data pipelines.
DataConnect provides an intelligent, low-code integration platform to address complex use cases with automated, intuitive, and reusable integrations. DataConnect includes a graphical studio for visually designing data pipelines, mapping data fields, and transforming data. Data preparation pipelines can be centrally managed, lowering administration costs.
The Vector database makes it easier to perform high-speed data analysis due to its columnar storage capability, which minimizes the need for pre-existing data indexes.
The Actian Data Platform runs on-premises and on multiple cloud platforms, including AWS, Azure, and Google Cloud, so you can run your analytics wherever your data resides.