Data pipeline orchestration

- Let's now talk about the data pipeline orchestration pattern, which addresses problems associated with manually defining, carrying out, and managing complex data workflows, which can lead to errors and inconsistencies. A data pipeline orchestration platform overcomes these problems by automating and managing data workflows that involve multiple steps, dependencies, and data sources. A data pipeline is essentially like a series of steps that begins with raw data as input, and then turns that raw data into usable data. A data pipeline orchestration platform allows us to define the workflow logic of a data pipeline and then automate and manage the steps in that workflow. The execution of the workflow can be scheduled and monitored. This type of workflow is not limited to automating the retrieval of data from external sources and the flow of that data to the AI system. It can also interact and automate tasks directly within external environments, such as big data systems, data warehouses, data lakes, and other platforms and data sources. A data pipeline orchestration platform will come with a library of prebuilt tasks that can be used for common data operations like extracting and transforming data. It also allows for the creation of custom automated tasks. These types of orchestration platforms are naturally equipped with a collection of data connectors that allow them to interact with a variety of databases and data platforms. A data pipeline orchestration platform will be able to not only connect to various infrastructure resources, such as on-premise and cloud servers, but it'll also be able to dynamically allocate these resources to ensure specific tasks get the compute power they need, and also for dynamic scaling purposes. There will be a dedicated metadata repository and associated tools that maintain a catalog of data sources, tasks, pipelines, and other data assets, and further record data flows from source to destination. There will also be various security and governance tools and controls to administer access to tasks and data within a given pipeline, and to also provide encryption for data at rest and in transit along with auditing and logging capabilities. In chapter two, we'll be covering a separate platform dedicated to model pipeline orchestration. Even though that platform has similar features to automate model training workflows, it's worth noting that the workflow of a data pipeline orchestration platform is commonly associated with model training in that it automates the preparation of the training data and can then even trigger the model training process as soon as that data is ready. Let's now take a brief look at some common built-in AI capabilities that data pipeline orchestration platforms can have. AI-enabled resource optimization, whereby a predictive AI system is used to predict the resource requirements for a given task. Those resources are then dynamically allocated. AI-enabled failure prediction whereby a predictive AI system analyzes historical pipeline executions to then predict potential failures or problems that can be proactively addressed. AI-enabled data quality prediction whereby, again, a predictive AI system analyzes past data source behaviors and errors to predict possible data quality issues that can be addressed by automated data validation and data cleansing. AI-enabled pipeline optimization whereby a generative AI system is used to recommend workflow enhancements, such as changing task sequences and resource allocations. AI-enabled co-generation whereby a generative AI system is used to produce programming or script code needed for specific tasks or data processing requirements. AI-enabled data synthesis whereby we, again, use a generative AI system to create synthetic data that we use to test or validate our data pipeline before it goes into production usage. Data orchestration platform products vary in size, complexity, and cost. It's important to match the product to your requirements. Bringing in a large and complex platform for a simple pipeline workflow can be overkill and can burden your organization with unnecessary costs and effort. On the other hand, a low cost platform with limited features can inhibit your ability to automate more complex workflows. And of course, it's best to ensure that you even need a data orchestration platform at all. Some simpler workflows can be automated using scripts and task scheduling tools instead.