Microsoft Azure: Metadata Driven Data Pipelines Framework
YASH Technologies
This workshop will help you leverage & adapt our Metadata Driven Data Pipelines Framework to ingest data from multiple data sources and curate that data throughout its lifecycle.
This workshop will help you leverage & adapt our Metadata Driven Data Pipelines Framework to ingest data from multiple data sources and curate that data throughout its lifecycle.
One of the most common ELT performance issues is the poor quality of the source data. This can include missing, inaccurate, inconsistent, or duplicate data that can cause errors, delays, or rework in the ELT process. Your workload can slow down dramatically if your ELT system lacks computing resources such as memory or storage. When performing ELT, out-of-date, inaccurate, and duplicate records are just a few data quality issues. Your ELT workflow must verify that you’re pulling the newest information possible and not extracting the same information from multiple sources. The ELT needs of almost every organization will evolve. This includes changes in data formats and connections and increased data volume and velocity (i.e., the amount of new data and the speed at which it arrives).
Our Metadata-Driven Data Pipelines Framework solves these problems by offering pre-built reusable modular data pipelines templates for ingestion, curation and orchestration for multiple data sources and curating the data to overcome significant data quality issues.
The Components of Metadata-Driven Data Pipelines Framework
- ADF Pipeline INGESTION TEMPLATES: Pre-built templates, with supporting deployment guidelines, are available to DA Products for consumption, enabling ingestion of various source system types (E.g. SQL, ODBC, File based, etc.).
- Databricks CURATION TEMPLATES: The curation engine follows a metadata driven approach, using JSON configuration files, which allow DA Products to customize the templates/ patterns to their requirements.
- CONFIG TEMPLATES: The curation engine follows a metadata driven approach, using JSON configuration files, which allow DA Products to customize the templates/ patterns to their requirements.
- DEVOPS AND DATAOPS TEMPLATES: Enabling Continuous Integration/ Continuous Deployment (CI/CD) through repeatable and re-usable processes, resulting in a consistent approach across DA Products.
- TESTING FRAMEWORK: A well-defined and repeatable testing framework to support DA Products implementation of high-quality solutions.
- Microsoft Azure Managed Apache Airflow Template for Orchestration: Pre-built JINJA templates, designed to streamline the creation of DAGs in Microsoft Azure Managed Apache Airflow, offer a foundation for efficiently orchestrating complex pipelines.
Advantages of Metadata Driven Data Pipelines Framework in Microsoft Azure.
- Speed to Delivery: Our Metadata-Driven Data Pipelines Framework provides pre-defined templates for Ingestion, Curation, Configuration, DevOps, Testing and Orchestration which helps in reducing the cost up to 57%.
- Consistency: Consistency in the codebase enhances maintainability, making it easier to manage and update the framework over time, ensuring standardization. This approach offers unique agility in developing or modifying configurations.
- Optimization: By leveraging robust capabilities and best practices by creating pre-built templates ensures high throughput and low latency in data processing.
- Reusability: Modular design and pre-built libraries promote the reuse of components across multiple projects, reducing duplication of effort.
- Efficiency: Enhances overall team efficiency in the data supply chain and frees up time to focus on developing new features and improvements.
How will YASH assist you in overcoming these challenges?
At YASH, we comprehensively evaluate your current ELT processes, data sources, and data management practices to identify limitations and areas for enhancement. Our team of experts collaborates with you to define your objectives within a metadata-driven ELT framework, aiming for heightened agility, robust data governance, and streamlined data processing. Together, we determine the types of metadata to manage (technical, operational, business) and establish protocols for metadata collection, storage, and utilization. We establish a secure and scalable centralized repository for all metadata, ensuring accessibility and reliability.
Leveraging Microsoft Azure, we seamlessly implement the ELT framework and provide tailored training and resources to empower your team to utilize the new system effectively. We remain committed to optimizing performance by continuously monitoring ELT processes, soliciting feedback, and making iterative enhancements to ensure sustained efficiency and efficacy. For more information, contact