Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Alation

FEBRUARY 20, 2020

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

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 Engineer Data Engineering Data Engineering 52

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. However, these tools have functional gaps for more advanced data workflows.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 28, 2024

Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

APRIL 6, 2023

To become a successful Data Engineer, you need to have strong knowledge of programming, statistics, analytical skills, and an understanding of Big Data. How to Become an Azure Data Engineer? Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

JANUARY 28, 2025

Data Lakes: These store raw, unprocessed data in its original format. 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. time, product) and facts (e.g.,

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

JUNE 7, 2024

Oracle Data Integrator Oracle Data Integrator (ODI) is designed for building, deploying, and managing data warehouses. Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Read More: Advanced SQL Tips and Tricks for Data Analysts.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

DagsHub

OCTOBER 23, 2024

NoSQL Databases NoSQL databases do not follow the traditional relational database structure, which makes them ideal for storing unstructured data. They allow flexible data models such as document, key-value, and wide-column formats, which are well-suited for large-scale data management.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? These processes are essential in AI-based big data analytics and decision-making.

AI 52

AI AI Data Lakes Database 52