Data Pipeline Data Visualization Power BI 
Data Science Dojo
SEPTEMBER 11, 2024
Let’s explore each of these components and its application in the sales domain: Synapse Data Engineering: Synapse Data Engineering provides a powerful Spark platform designed for large-scale data transformations through Lakehouse. Here, we changed the data types of columns and dealt with missing values.
phData
MAY 25, 2023
How to Optimize Power BI and Snowflake for Advanced Analytics Spencer Baucke May 25, 2023 The world of business intelligence and data modernization has never been more competitive than it is today. Table of Contents Why Discuss Snowflake & Power BI?
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Pickl AI
MARCH 19, 2025
Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Below are 20 essential tools every data engineer should know.
Data Science Dojo
JULY 3, 2024
Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.
ODSC - Open Data Science
MARCH 12, 2025
Data Engineerings SteadyGrowth 20182021: Data engineering was often mentioned but overshadowed by modeling advancements. 20222024: As AI models required larger and cleaner datasets, interest in data pipelines, ETL frameworks, and real-time data processing surged.
ODSC - Open Data Science
FEBRUARY 6, 2024
Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time.
IBM Journey to AI blog
SEPTEMBER 19, 2023
To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model.
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