Data Engineering, Data Modeling, Data Models and Data Pipeline

Data Engineering

Data Modeling

Data Models

Data Pipeline

CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

MAY 20, 2024

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

Data Pipeline

Data Pipeline Data Warehouse Azure Data Lakes

Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

The Project Clinic: Assessing Project Health, Planning, and Execution

Leading the Development of Profitable and Sustainable Products

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Becoming a Data Engineer: 7 Tips to Take Your Career to the Next Level

Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

The Project Clinic: Assessing Project Health, Planning, and Execution

Leading the Development of Profitable and Sustainable Products

MORE WEBINARS

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

What Are dbt Artifacts

phData

FEBRUARY 8, 2024

Data Modeling, dbt has gradually emerged as a powerful tool that largely simplifies the process of building and handling data pipelines. dbt is an open-source command-line tool that allows data engineers to transform, test, and document the data into one single hub which follows the best practices of software engineering.

Data Modeling

Data Modeling Data Models Database Data Warehouse

Where Does Fivetran Fit into The Modern Data Stack?

phData

JULY 17, 2023

This is where Fivetran and the Modern Data Stack come in. Fivetran is a fully-automated, zero-maintenance data pipeline tool that automates the ETL process from data sources to your cloud warehouse. Snowflake Data Cloud Replication Transferring data from a source system to a cloud data warehouse.

Data Warehouse

Data Warehouse Data Pipeline Cloud Data ETL

Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

SQL

SQL Data Observability Data Quality Data Pipeline

Find Your AI Solutions at the ODSC West AI Expo

ODSC - Open Data Science

OCTOBER 15, 2023

Elementl / Dagster Labs Elementl and Dagster Labs are both companies that provide platforms for building and managing data pipelines. Elementl’s platform is designed for data engineers, while Dagster Labs’ platform is designed for data scientists. ArangoDB is designed to be scalable, reliable, and easy to use.

Machine Learning

Machine Learning Machine Learning Data Pipeline AI

The Data Engineer’s Roadmap

Dataversity

SEPTEMBER 28, 2022

Data engineering is a fascinating and fulfilling career – you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. The journey to becoming a successful data engineer […].

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

The Official Machine Learning and AI Platform of Hacktoberfest 2023

DagsHub

SEPTEMBER 10, 2023

DagsHub is a centralized platform to host and manage machine learning projects, including code, data, models, experiments, annotations, model registry, and more! Intermediate Data Pipeline : Build data pipelines using DVC for automation and versioning of Open Source Machine Learning projects.

Machine Learning

Machine Learning Machine Learning Data Pipeline AI

What are Snowflake Dynamic Tables?

phData

NOVEMBER 2, 2023

Managing data pipelines efficiently is paramount for any organization. The Snowflake Data Cloud has introduced a groundbreaking feature that promises to simplify and supercharge this process: Snowflake Dynamic Tables. Flexibility: Dynamic tables allow batch and streaming pipelines to be specified in the same way.

Data Pipeline

Data Pipeline SQL Data Warehouse Data Engineering

Generative AI in Software Development

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI AI Data Analysis Data Analysis

How to Optimize Power BI and Snowflake for Advanced Analytics

phData

MAY 25, 2023

Model Your Data Appropriately Once you have chosen the method to connect to your data (Import, DirectQuery, Composite), you will need to make sure that you create an efficient and optimized data model. Here are some of our best practices for building data models in Power BI to optimize your Snowflake experience: 1.

Power BI

Power BI Analytics Analytics Azure

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality

Data Quality Data Lakes Data Warehouse Business Intelligence

LLMOps vs. MLOps: Understanding the Differences

Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. LLMOps is MLOps for LLMs.

ML ML AI AI

Unlocking Tabular Data’s Hidden Potential

ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. Making data engineering more systematic through principles and tools will be key to making AI algorithms work.

Data Scientist

Data Scientist Data Science Deep Learning Deep Learning

Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

Data Science

Data Science Analytics Analytics Data Scientist

What Industries are Hiring for Different Jobs in AI

ODSC - Open Data Science

APRIL 26, 2023

Tools such as the mentioned are critical for anyone interested in becoming a machine learning engineer. Data Engineer Data engineers are the authors of the infrastructure that stores, processes, and manages the large volumes of data an organization has.

Data Analyst

Data Analyst Machine Learning Machine Learning Power BI

How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

AI AI Data Warehouse ML

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai Can you render audio/video?

Machine Learning

Machine Learning Machine Learning ML ML

Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists.

Data Warehouse

Data Warehouse Business Intelligence Business Intelligence Database

The Ultimate Modern Data Stack Migration Guide

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

Data Warehouse SQL Analytics Analytics

ML Collaboration: Best Practices From 4 ML Teams

The MLOps Blog

DECEMBER 28, 2022

Team composition The team comprises domain experts, data engineers, data scientists, and ML engineers. Team composition The team comprises data pipeline engineers, ML engineers, full-stack engineers, and data scientists.

ML ML Data Scientist Machine Learning

Data Governance for Dummies: Your Questions, Answered

Alation

FEBRUARY 17, 2023

I suggest building out a RACI framework that assigns core activities across these key roles: (1) Data Owner; (2) Business Data Steward; (3) Technical (IT) Data Steward; (4) Enterprise Data Steward; (5) Data Engineer; and (6) Data Consumer. Communication is essential. This is a very good thing.

Data Governance

Data Governance Data Quality Data Analyst Data Pipeline

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning

Machine Learning Machine Learning ML ML