ODSC - Open Data Science

SEPTEMBER 27, 2023

Flexibility and Agility Data lakes provide flexibility, enabling organizations to store diverse data types without worrying about immediate data modeling. This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

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. Most developers are familiar with Git for source code versioning.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

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 52

Azure Data Engineering Data Engineering Data Engineer 52

Pickl AI

JUNE 7, 2024

Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Data Integrator Studio: Provides a graphical interface for business users and developers to manage data integration tasks. Scalability: Designed to handle large volumes of data efficiently.

ETL 40

ETL Data Pipeline Data Quality Data Warehouse 40

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 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

JANUARY 28, 2025

They are useful for big data analytics where flexibility is needed. Data Modeling Data modeling involves creating logical structures that define how data elements relate to each other. This includes: Dimensional Modeling : Organizes data into dimensions (e.g., time, product) and facts (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g.,

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

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.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52