Data Hurdles

Data is a valuable asset in modern business, and data pipelines are essential for making it more usable and consumable. We'll delve into the concept of data pipelines, their components, and their importance. We'll also explore the challenges of building and operating data pipelines, as well as the best practices and tools to overcome them.

Data pipelines can be compared to water pipelines, where raw data is like dirty water that needs to be cleaned and enriched through pipes to make it usable. The purpose of data pipelines is to move data from an origin to a destination while transforming it into a more usable form. The three main components of a data pipeline are the origin, destination, and data flow. The origin is where the data originates, the destination is where it is delivered, and the data flow is the path the data takes from origin to destination.

There are two main types of data pipelines: batch and real-time. Batch pipelines process data in batches at specific intervals, while real-time pipelines process data as it is generated. Different technologies like Kafka and Lambda can be used for real-time data pipelines. The podcast emphasizes the importance of data pipelines in making data more consumable and easy to use in driving business processes and making critical decisions.

ETL (Extract, Transform, Load) is the process of extracting data, transforming it in some way, and loading it into another system. ETL helps facilitate data pipelines, and its different stages include extracting data, transforming it into a format suitable for analysis, performing calculations and summarizations, encrypting information or masking it, and loading the data into another system.
Data pipelines are managed by data stewards or engineers who use monitoring tools and governing tools to monitor the quality of data and ensure that the data pipeline is delivering high-quality data. Some of the advantages of a well-structured data pipeline include an increase in data quality, the ability to create different data flows for different purposes, and the ability to manage and maintain these pipelines for different use cases.

However, building and operating data pipelines can be a complex and massive task, especially for engineers who are building their first pipeline. To start, it is important to do research, understand the current ecosystem, and clearly define business objectives. Reusability and scalability are also important factors to consider, as well as monitoring the quality of the data as it flows through the system. It is crucial to involve the right people and ask the right questions, as well as using managed systems unless you have the expertise to build them yourself. Building a system with just one person or a small group is not recommended, and it is essential to think of it as a product that needs to be maintained and supported.

Data quality is a critical factor in data pipelines. Michael Burke advises starting with identifying the consumers of the data and their use cases, considering the risk and cost associated with different types of data, and democratizing data while maintaining control over critical pipelines. He emphasizes the importance of talking to the right stakeholders and representing their needs, and warns against building a data pipeline for a scoped view of things. Data quality metrics such as accuracy, consistency, completeness, timeliness, validity, and uniqueness depend on the context of the data and its use cases.
Managing data pipelines in organizations can also be challenging. It is essential to always hire and teach to ensure a constant supply of talent, minimize the centers of data, maintain data lineage, and keep track of the context of data. Understanding the business value of data and reminding oneself of it constantly is crucial.

Data pipelines are essential for making data more usable and consumable in modern business. They are critical components of ETL processes, and their two main types are batch and real-time. Data stewards and engineers manage data pipelines using monitoring and governing tools to ensure high-quality data delivery