Data Pipeline category
August 30, 2023

Data Testing Tools: Key Capabilities and 6 Tools You Should Know

3 min read - Why are data testing tools important? Improved data quality The primary goal of using data testing tools is to enhance the overall quality of an organization’s data assets. By identifying inconsistencies, inaccuracies or duplicates within datasets early in the development process, these tools enable teams to address issues before they escalate into more significant problems that can impact business operations. Accelerated decision-making In today’s fast-paced business environment, where decisions need to be made quickly based on accurate information, having access…

July 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices

4 min read - What is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. This can be achieved manually, or automatically using a combination of software and hardware tools designed specifically for this task. Data can come from many different sources, and in many different formats—from structured databases to unstructured documents. These sources might include external data like social media feeds, internal data like logs or reports, or even real-time data…

July 19, 2023

What is ELT (Extract, Load, Transform)? A Beginner’s Guide

4 min read - ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. This transformation could involve cleaning, aggregating, or summarizing the data. ELT is commonly used in big data projects and real-time processing where speed and scalability are critical. In the past, data was often stored in a single location, such as a database or a data warehouse. However,…

July 17, 2023

DataOps vs. MLOps: Similarities, Differences, and How to Choose

2 min read - What is DataOps? DataOps, short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting. By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. The main objectives of DataOps include: Collaboration: Facilitating better communication between different teams…

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