Cloud Computing, Data Models and SQL

Cloud Computing

Data Models

SQL

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

Data Science Connect

JANUARY 27, 2023

In this blog post, we will be discussing 7 tips that will help you become a successful data engineer and take your career to the next level. Learn SQL: As a data engineer, you will be working with large amounts of data, and SQL is the most commonly used language for interacting with databases.

Data Engineering

Data Engineering Data Engineering Data Engineering Data Engineer

Why using Infrastructure as Code for developing Cloud-based Data Warehouse Systems?

Data Science Blog

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

Data Warehouse

Data Warehouse Azure SQL Database

Join 17,000+

professionals

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

Best Used Servers for Databases and Cloud Computing

Smart Data Collective

AUGUST 12, 2022

Investing in the Best Servers for Cloud Computing. Organizations that need servers for their databases or cloud computing can’t just go out and buy the first option that presents itself, though. What to look for in a server to meet your cloud computing needs. MS SQL Server. Server configuration.

Cloud Computing

Cloud Computing Database SQL System Architecture

TigerEye (YC S22) Is Hiring a Full Stack Engineer

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science

Computer Science Computer Science ML ML

Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

NOVEMBER 15, 2023

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

Data Models

Data Models Data Modeling Business Intelligence Business Intelligence

Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science

Data Science Exploratory Data Analysis Machine Learning Machine Learning

How to Optimize Power BI and Snowflake for Advanced Analytics

phData

MAY 25, 2023

Snowflake is a cloud computing–based data cloud company that provides data warehousing services that are far more scalable and flexible than traditional data warehousing products. The June 2021 release of Power BI Desktop introduced Custom SQL queries to Snowflake in DirectQuery mode.

Power BI

Power BI Analytics Analytics Azure

Azure Data Engineer Jobs

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Hands-on experience working with SQLDW and SQL-DB.

Azure

Azure Data Engineering Data Engineering Data Engineering

The innovators behind intelligent machines: A look at ML engineers

Dataconomy

MAY 2, 2023

Familiarity with machine learning frameworks, data structures, and algorithms is also essential. Additionally, expertise in big data technologies, database management systems, cloud computing platforms, problem-solving, critical thinking, and collaboration is necessary.

ML ML Machine Learning Machine Learning

dbt and Sigma Integration

phData

JUNE 27, 2023

The modern data stack (MDS) has seen massive changes over the past few decades, fueled by technological advances and new platforms. As a result, we are presented with specialized data platforms, databases, and warehouses. All of which have a specific role used to collect, store, process, and analyze data.

SQL

SQL Database Data Quality Data Warehouse

Discover the Most Important Fundamentals of Data Engineering

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering

Data Engineering Data Engineering Data Engineering Data Engineer

Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning

Machine Learning Machine Learning Data Science Big Data

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

Mlearning.ai

FEBRUARY 16, 2023

Data warehousing is a vital constituent of any business intelligence operation. Companies can build Snowflake databases expeditiously and use them for ad-hoc analysis by making SQL queries. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.