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. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

AWS Machine Learning Blog

JANUARY 5, 2024

Collaboration – Data scientists each worked on their own local Jupyter notebooks to create and train ML models. They lacked an effective method for sharing and collaborating with other data scientists. This has helped the data scientist team to create and test pipelines at a much faster pace.

AWS 137

AWS Data Science ML ML 137

ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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

AWS Machine Learning Blog

AUGUST 21, 2023

We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model. Tina, a data scientist on the sales team.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Alation

OCTOBER 27, 2022

Like so many data teams out there, we need to maximize our throughput whilst ensuring quality. Jason: I’m curious to learn about your modern data stack. Adrian Lievano, Senior Data Scientist, Alation : Most of our data sources are connected and extracted using Fivetran , and then transported to raw storage in Snowflake.

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

Pickl AI

MARCH 19, 2025

Data Science focuses on analysing data to find patterns and make predictions. Data engineering, on the other hand, builds the foundation that makes this analysis possible. Without well-structured data, Data Scientists cannot perform their work efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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 Engineer Data Engineering Data Engineering 40

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? For Azure Data Engineer, there are various skills required.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

OCTOBER 9, 2024

Confirmed sessions related to software engineering include: Building Data Contracts with Open-Source Tools Chronon — Open Source Data Platform for AI/ML Creating APIs That Data Scientists Will Love with FastAPI, SQLAlchemy, and Pydantic Using APIs in Data Science Without Breaking Anything Don’t Go Over the Deep End: Building an Effective OSS Management (..)

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 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 Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52