In today’s data-driven landscape, maintaining high-quality data is essential for informed decision-making. This article explores effective data testing strategies, such as automated testing and real-time monitoring, to ensure data accuracy, completeness, and consistency. By implementing robust practices, organizations can maintain data integrity, boost decision-making, and improve business performance.
A Data Pipeline is a set of steps that Extract, Load and Transform data for consumption by the end user. As part of the blog series on Data Pipelines, we spoke about Data Ingestion and the different open-source and commercial players. In this blog we will talk about different data ingestion methods The need for […]
In the previous blog series, we defined data pipelines, the types of data pipelines, and the data pipeline components. We identified the three main pieces of a data pipeline: Extract, Load, and Transform. In this blog, we focus on “Extract”, also referred to as Data Ingestion. Data Ingestion Data Ingestion is the movement of data […]
The question of ETL vs ELT is a recurring one in the world of data analytics. Historically, data engineers provide data consumers with processed data ready for consumption in a data warehouse. The process of delivering such processed data to consumers can be thoughts of as Extract, Transform, and Load (ETL). This blog demonstrated hoe […]
As part of the Data Pipeline series, in part one and two of the series, we talked about data pipeline and components of data pipeline. The third part deals with types of data pipelines. The type of data pipeline is related to the need for fresh data. Data pipeline types are traditional (batch) and real-time. […]
In our first article in the series we talked about Data Pipeline. A Data Pipeline is the process of taking data from source and getting it ready for consumption by the end users. This blog intends to explain the different data pipeline components Data Pipeline consists of eight components as shown in the diagram below: […]
Data Pipeline is a sequence of steps that deliver consumable data to the end users. Why do we need a sequence of steps? In the present world, data comes from diverse sources in different formats. It is the job of a data engineer to make consumable data available to various consumers. Automated orchestration of these […]
Data exists in one of three states: Data at Rest, Data in Motion and Data in Use. When we understand these three states, it lays the foundation for how to extract value from that data to support business operations. This blog intends to introduce the concepts and lay a basis for the upcoming data engineering […]
Machine Learning and Data Science are two terms that are used interchangeably. Both seem to indicate a way for machines to learn the output from the given input. However, the terms have different meanings and signify a different phase of the machine learning life cycle. Why Should You Care? Two words that help to […]