Towards AI

FEBRUARY 11, 2025

Consider these common scenarios: A perfect validation script cant fix inconsistent data entry practices The most robust ETL pipeline cant resolve disagreements about business rules Real-time quality monitoring cant replace clear data ownership. Why Technical Band-Aids Fail These solutions work until they dont.

Data Quality 54

Data Quality Data Engineering Data Engineer Data Engineering 54

Pickl AI

JULY 25, 2024

Talend A data integration platform that offers a suite of tools for data ingestion, transformation, and management. AWS Glue A fully managed ETL service that makes it easy to prepare and load data for analytics. It automates the process of data discovery, transformation, and loading.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Dataconomy

JULY 28, 2023

Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

JULY 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

phData

FEBRUARY 7, 2024

Snowpark Use Cases Data Science Streamlining data preparation and pre-processing: Snowpark’s Python, Java, and Scala libraries allow data scientists to use familiar tools for wrangling and cleaning data directly within Snowflake, eliminating the need for separate ETL pipelines and reducing context switching.

Python 52

Python ML ML SQL 52

Pickl AI

NOVEMBER 14, 2023

So, let me present to you an Importing Data in Python Cheat Sheet which will make your life easier. For initiating any data science project, first, you need to analyze the data. You probably already know that there are a bunch of ways to do that, depending on what kind of files you are working with.

Python 52

Python SQL Database Data Analysis 52

DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. is similar to the traditional Extract, Transform, Load (ETL) process. It operates in three stages: Extract unstructured data from a source.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Women in Big Data

JANUARY 25, 2025

Key Points: Data Acquisition: Automated data collection from APIs, IoT devices, and databases. Live quality checks ensure clean data processing. Data enrichment with geodata and external market data. Data Management: AI cleans duplicates and errors while optimizing data integration (ETL processes).

Data Governance 52

Data Governance Big Data Big Data Data Quality 52