Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JUNE 7, 2024

IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Key Features: Graphical Framework: Allows users to design data pipelines with ease using a graphical user interface. Read Further: Azure Data Engineer Jobs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. It does not support the ‘dvc repro’ command to reproduce its data pipeline.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Use Multiple Data Models With on-premise data warehouses, storing multiple copies of data can be too expensive.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

DagsHub

APRIL 21, 2024

Amazon SageMaker pricing is based on a pay-as-you-go model, with costs calculated based on factors such as instance type, storage usage, and training hours. Similar to SageMaker, Azure ML offers a range of tools and services for the entire machine learning lifecycle, from data preparation and model development to deployment and monitoring.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

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

phData

SEPTEMBER 25, 2024

As a fully managed service, Snowflake eliminates the need for infrastructure maintenance, differentiating itself from traditional data warehouses by being built from the ground up. It can be hosted on major cloud platforms like AWS, Azure, and GCP.

ETL 52

ETL Database Data Warehouse Analytics 52

DagsHub

AUGUST 29, 2024

Data can change a lot, models may also quickly evolve and dependencies become old-fashioned which makes it hard to maintain consistency or reproducibility. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code. or other dedicated backup servers.

ML 52

ML ML Python Machine Learning 52

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

phData

JULY 18, 2023

Enter dbt dbt provides SQL-centric transformations for your data modeling and transformations, which is efficient for scrubbing and transforming your data while being an easy skill set to hire for and develop within your teams. It should also enable easy sharing of insights across the organization.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

Towards AI

JANUARY 22, 2025

Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs. These two languages cover most data science workflows. Additionally, languages like DAX can be helpful for specific use cases involving data models and dashboards.

Data Scientist 105

Data Scientist EDA Exploratory Data Analysis AI 105

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 Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52