DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. The global data warehouse as a service market was valued at USD 9.06

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

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.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DagsHub

APRIL 21, 2024

Best MLOps Tools & Platforms for 2024 In this section, you will learn about the top MLOps tools and platforms that are commonly used across organizations for managing machine learning pipelines. Data storage and versioning Some of the most popular data storage and versioning tools are Git and DVC.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

IBM Journey to AI blog

AUGUST 12, 2024

Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes). Massive, in fact.

Big Data 106

Big Data Big Data ML ML 106

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Let’s unlock the power of ETL Tools for seamless data handling.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

ODSC - Open Data Science

JANUARY 18, 2024

Data engineers will also work with data scientists to design and implement data pipelines; ensuring steady flows and minimal issues for data teams. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. Learn more about the cloud.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

JULY 2, 2024

Developers can seamlessly build data pipelines, ML models, and data applications with User-Defined Functions and Stored Procedures. If your data pipeline requirements are quite straightforward—i.e., You have different developers working on building data pipelines/UDFs/stored procedures in the same environment.

Python 52

Python SQL Data Pipeline ML 52

ODSC - Open Data Science

OCTOBER 13, 2024

By versioning datasets in the same way we version code, data teams can experiment, roll back changes, and merge data pipelines safely, all without duplicating data or slowing down operations.

Data Lakes 40

Data Lakes Database Data Pipeline SQL 40

phData

JULY 5, 2024

At the time of writing this blog, the year is 2024, and companies that have not yet adopted Gen AI may be feeling the pressure of being left behind. The generative AI solutions from GCP Vertex AI, AWS Bedrock, Azure AI, and Snowflake Cortex all provide access to a variety of industry-leading foundational models.

AI 52

AI AI Database AWS 52

phData

DECEMBER 4, 2023

This blog was originally written by Erik Hyrkas and updated for 2024 by Justin Delisi This isn’t meant to be a technical how-to guide — most of those details are readily available via a quick Google search — but rather an opinionated review of key processes and potential approaches. Use with caution, and test before committing to using them.

Clustering 98

Clustering Database SQL Data Pipeline 98

phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. In this example, the secret is an API key, which will be used later on in the pipeline.

Python 52

Python ETL AWS Database 52

AWS Machine Learning Blog

MARCH 15, 2024

This approach can help heart stroke patients, doctors, and researchers with faster diagnosis, enriched decision-making, and more informed, inclusive research work on stroke-related health issues, using a cloud-native approach with AWS services for lightweight lift and straightforward adoption. Stroke victims can lose around 1.9

AWS 131

AWS ML ML Data Silos 131

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 Engineer Data Engineering Data Engineering 52