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 130

Data Engineering Data Engineering Data Engineering Data Engineer 130

KDnuggets

AUGUST 21, 2023

A comprehensive comparison between OLAP and OLTP systems, exploring their features, data models, performance needs, and use cases in data engineering.

Data Engineering 218

Data Engineering Data Engineer Data Engineering Data Engineering 218

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. It offers full BI-Stack Automation, from source to data warehouse through to frontend.

Data Pipeline 262

Data Pipeline Data Warehouse Azure Data Lakes 262

Data Science Dojo

MAY 10, 2023

Top 10 Professions in Data Science: Below, we provide a list of the top data science careers along with their corresponding salary ranges: 1. Data Scientist Data scientists are responsible for designing and implementing data models, analyzing and interpreting data, and communicating insights to stakeholders.

Data Science 275

Data Science Data Scientist Database Administration Machine Learning 275

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 Modeling 245

Data Modeling Data Models Business Intelligence Business Intelligence 245

Analytics Vidhya

MARCH 2, 2023

Introduction Data normalization is the process of building a database according to what is known as a canonical form, where the final product is a relational database with no data redundancy. More specifically, normalization involves organizing data according to attributes assigned as part of a larger data model.

Database 324

Database Data Modeling Data Models Analytics 324

Analytics Vidhya

MARCH 1, 2023

It manages huge volumes of data across many commodity servers, ensures fault tolerance with the swift transfer of data, and provides high availability with no single point of failure.

Big Data 317

Big Data Big Data Database Data Modeling 317

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Data Science Dojo

OCTOBER 31, 2024

Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes. This role builds a foundation for specialization.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

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 156

Computer Science Computer Science ML ML 156

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 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

Data Science Blog

SEPTEMBER 19, 2023

Streamlined Collaboration Among Teams Data Warehouse Systems in the cloud often involve cross-functional teams — data engineers, data scientists, and system administrators. This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure.

Data Warehouse 259

Data Warehouse Azure SQL Database 259

phData

DECEMBER 18, 2023

Data engineering refers to the design of systems that are capable of collecting, analyzing, and storing data at a large scale. In manufacturing, data engineering aids in optimizing operations and enhancing productivity while ensuring curated data that is both compliant and high in integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Data Science Dojo

JANUARY 10, 2023

All data roles are identical It’s a common data science myth that all data roles are the same. So, let’s distinguish between some common data roles – data engineer, data scientist, and data analyst. Data scientists only work on predictive modeling Another myth!

Data Science 195

Data Science Data Scientist Data Analyst Machine Learning 195

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 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

AUGUST 11, 2023

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. However, data collection and analysis have been commonplace in the healthcare sector for ages. Data Engineering in day-to-day hospital administration can help with better decision-making and patient diagnosis/prognosis.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

JULY 22, 2024

Welcome to Beyond the Data, a series that investigates the people behind the talent of phData. Data Engineer at phData. Data Engineer? As a Senior Data Engineer, I wear many hats. On the technical side, I clean and organize data, design storage solutions, and build transformation pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

ODSC - Open Data Science

JANUARY 11, 2024

Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven Data Modeling How To Get Started With Building AI in High-Risk Industries This guide will get you started building AI in your organization with ease, axing unnecessary jargon and fluff, so you can start today.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data. Data is stored in HDFS and is accessed via Hive, which provides a tabular interface to the data and integrates with Spark SQL.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

Azure 52

Azure ML ML Data Models 52

Tableau

JULY 25, 2024

Spencer Czapiewski July 25, 2024 - 5:54pm Thomas Nhan Director, Product Management, Tableau Lari McEdward Technical Writer, Tableau Expand your data modeling and analysis with Multi-fact Relationships, available with Tableau 2024.2. Sometimes data spans multiple base tables in different, unrelated contexts.

Tableau 76

Tableau Data Modeling Data Models Data Silos 76

Smart Data Collective

MAY 29, 2023

Three Different Analysts Data analysis as a whole is a very broad concept which can and should be broken down into three separate, more specific categories : Data Scientist, Data Engineer, and Data Analyst. Data Scientist These employees are programmers and analysts combined.

Data Analyst 104

Data Analyst SQL Data Scientist Data Analysis 104

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 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

MARCH 3, 2023

Data Engineering A data engineers start to simplification Introduction A lot of time folks start directly jumping into KPIs ( Key Performace Indicators) without understanding the need for those KPIs. I have met with clients who have dumped all the data they had and never figured out what they really wanted to achieve.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Dataconomy

MAY 2, 2023

What do machine learning engineers do: They implement and train machine learning models Data modeling One of the primary tasks in machine learning is to analyze unstructured data models, which requires a solid foundation in data modeling. How data engineers tame Big Data?

ML 110

ML ML Machine Learning Machine Learning 110

AWS Machine Learning Blog

OCTOBER 20, 2023

Collectively, these modules address governance across various dimensions, such as infrastructure, data, model, and cost. Reference architecture modules The reference architecture comprises eight modules, each designed to solve a specific set of problems.

ML 135

ML ML AWS Data Lakes 135

ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

ODSC - Open Data Science

MAY 10, 2023

Data-centric AI, in his opinion, is based on the following principles: It’s time to focus on the data — after all the progress achieved in algorithms means it’s now time to spend more time on the data Inconsistent data labels are common since reasonable, well-trained people can see things differently.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

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 84

Power BI Analytics Analytics Azure 84

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

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

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 52

Data Modeling Data Models Data Warehouse Database 52