This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
For any data user in an enterprise today, dataprofiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of dataprofiling, top use cases, and share important techniques and best practices for dataprofiling today.
According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of datalakes and data warehouses. Most organizations do not utilize the entirety of the data […].
Thoughtworks says data mesh is key to moving beyond a monolithic datalake. Spoiler alert: data fabric and data mesh are independent design concepts that are, in fact, quite complementary. Thoughtworks says data mesh is key to moving beyond a monolithic datalake 2. Gartner on Data Fabric.
LakeFS LakeFS is an open-source platform that provides datalake versioning and management capabilities. It sits between the datalake and cloud object storage, allowing you to version and control changes to datalakes at scale. Share features across the organization.
The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.
Dataflows allow users to establish source connections and retrieve data, and subsequent data transformations can be conducted using the online Power Query Editor. In this blog, we will provide insights into the process of creating Dataflows and offer guidance on when to choose them to address real-world use cases effectively.
ETL data pipeline architecture | Source: Author Data Discovery: Data can be sourced from various types of systems, such as databases, file systems, APIs, or streaming sources. We also need dataprofiling i.e. data discovery, to understand if the data is appropriate for ETL.
Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a datalake.
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content