Data Quality Implementation Tips
🔥 New on the blog: Data Quality Dimensions & How to Implement Them 🔍 I just published a detailed post on why data quality matters and how to implement data quality checks across your systems — covering key dimensions like accuracy, consistency, completeness, timeliness, validity, uniqueness, and more. These fundamentals help teams trust their data…
Read MoreDatabricks
Check out the post related to “Databricks” under “Databricks” Menu. Multiple Topics to be added.
Read MoreQlik Replicate Source latency
Source latency is high, typically indicates that the changes made in the source database are taking a significant amount of time to be reflected in the replicated data at the target. Source latency represents the delay between a change occurring in the source database and the corresponding update being applied to the target. Here are…
Read MoreData Modeling, Data Warehouse & Data Lake
Check out the post related to “Data & Dimensional Modeling” under “Data modeling” Menu Data & Dimensional Modeling, Data Warehouse & Data Lake
Read MoreJupyter Notebook Tips
Check out the post related to “Working with Jupyter Notebook – Tips” under python Menu
Read MorePython
Python Installation There are two ways to download and install Python Useful commands: pip list To get the installed packages python – – version To check the python version pip show pandas To display Pandas Version print(pandas.__version__) To display Pandas Version pandas.show_versions() It comprises info about hosting OS, Pandas version, and versions of other installed…
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