Pickl AI

DECEMBER 9, 2024

First, lets understand the basics of Big Data. Key Takeaways Understand the 5Vs of Big Data: Volume, Velocity, Variety, Veracity, Value. Familiarise yourself with essential tools like Hadoop and Spark. Practice coding skills in languages relevant to Big Data roles. What are the Main Components of Hadoop?

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

Pickl AI

JUNE 7, 2024

Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Introduction In today’s business landscape, data integration is vital. It is part of IBM’s Infosphere Information Server ecosystem.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. The right tool can significantly enhance efficiency, scalability, and data quality.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Precisely

JANUARY 19, 2023

The volume, velocity, and variety of data is growing exponentially. Organizations that can master the challenges of data integration, data quality, and context will be well positioned to identify opportunities and threats quickly, and then to take decisive action to gain competitive advantage.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

phData

FEBRUARY 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Moving historical data from a legacy system to Snowflake poses several challenges.

SQL 52

SQL Database Data Quality Data Warehouse 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

ODSC - Open Data Science

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52