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Generally, data-driven companies mainly struggle with data quality issues. Massive amounts of data, changing data storage requirements due to changing company regulations, and the various types of data sources and formats do make data management quite difficult for companies. Hence, the need for robust DataOps tools to automate and simplify data life-cycle phases is of utmost importance at this point. Here are, some of the most important DataOps tools that professionals can use in 2022.
Census: Census is the leading platform for operational analytics with reverse ETL (extract, transform, load), offering a single, trusted location to bring your warehouse data into your daily applications. It sits on top of your existing warehouse and connects the data from all of your go-to-market tools, allowing everyone in your company to act on good information without requiring any custom scripts or favors from IT.
Dbt: Dbt is a transformation workflow that allows organizations to deploy analytics code in a short time frame via software engineering best practices such as modularity, portability, CI/CD, and documentation. Dbt Core is an open-source command-line tool allowing anyone with a working knowledge of SQL to create high-quality data pipelines.
Databricks: Databricks Lakehouse Platform is a comprehensive data management platform that unifies data warehousing and AI use cases on a single platform via a web-based interface, command-line interface, and a software development kit. It includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics.
Mozart Data: Mozart Data is a simple out-of-the-box data stack that can help you consolidate, arrange, and get your data ready for analysis without requiring any technical expertise. It provides a web-based interface for data scientists to work with data in various formats, including CSV, JSON, and SQL. In addition, Mozart Data provides a flexible data modeling layer that allows data scientists to work with data in various ways.
Datafold: Datafold helps businesses prevent data catastrophes. It has the unique capacity to detect, evaluate, and investigate data quality concerns before they impact productivity. It combines machine learning with AI to provide analytics with real-time insights, allowing data scientists to make top-quality predictions from large amounts of data.
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