![]() ![]() Therefore, they have limited to no knowledge of the content of the task processing. Task-driven frameworks are highly decoupling the task management from the task processing. But we can classify them using three important features that interest us: Data-driven vs task-driven ![]() Here, we are only looking at code based frameworks and we are not presenting an exhaustive list. There are a lot of data processing frameworks. Note that the frameworks that we will compare generally allow to implement pipelines following the functional data engineering principles, and remember that we have also exposed our best practices when building pipelines at Kapernikov. In this way, these frameworks delegate their task processing to a robust and well supported platform and they can focus on the specifics of the management of data pipelines. It is therefore not surprising that most of the tools presented in this article have a way to connect to Kubernetes. ¹ Kubernetes (K8s) is a fully customizable platform for orchestration of containers. These orchestrators are defined natively in Kubernetes using custom resource definitions ( CRD). With the Kubernetes¹ platform, some solutions make use of the Kubernetes API to create their own orchestrator which is called a “Kubernetes operator”. Generally, the three components are managed internally by the framework and the end user doesn’t need to control them directly. A metadata store for saving the state to the pipeline.A scheduler that coordinates the different tasks.This orchestrator can be decomposed in three components: The pipeline is controlled by an orchestrator which ensures its correct execution. And the services can be streaming services that continuously deliver new data or predictive models that are used in tasks. The data can be structured or unstructured. These data artifacts can be files, in-memory data structures but also ephemeral services. Each task consumes inputs and produces outputs. The steps are units of work, in other words: tasks. In the following report, we refer to it as a pipeline (also called a workflow, a dataflow, a flow, a long ETL or ELT). Generally, these steps form a directed acyclic graph (DAG). You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure.įinally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.Components of a data processing frameworkĪ data processing framework is a tool that manages the transformation of data, and it does that in multiple steps. You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for importing data into data repositories. During this course, you will experience how ELT and ETL processing differ and identify use cases for both. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.īoth ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. ETL processes apply to data warehouses and data marts. The other contrasting approach is the Extract, Load, and Transform (ELT) process. One approach is the Extract, Transform, Load (ETL) process. After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data. ![]()
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