ODSC - Open Data Science

SEPTEMBER 27, 2023

This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure. This is particularly advantageous when dealing with exponentially growing data volumes. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Mlearning.ai

MAY 16, 2023

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data Ensuring data quality and integrity Data quality and integrity are essential for accurate data analysis. Data engineers are responsible for ensuring that the data collected is accurate, consistent, and reliable.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. These tools work together to facilitate efficient data management and analysis processes.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

NOVEMBER 15, 2023

What Is a Data Warehouse? On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

phData

FEBRUARY 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Moving historical data from a legacy system to Snowflake poses several challenges.

SQL 52

SQL Database Data Quality Data Warehouse 52

DagsHub

NOVEMBER 11, 2024

In general, this data has no clear structure because it may manifest real-world complexity, such as the subtlety of language or the details in a picture. Advanced methods are needed to process unstructured data, but its unstructured nature comes from how easily it is made and shared in today's digital world.

AI 52

AI AI Data Lakes Database 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. You can use stored procedures to handle complex ETL processes, make API calls, and perform data validation.

SQL 52

SQL Database Apache Hadoop Data Science 52

DagsHub

OCTOBER 23, 2024

They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

JANUARY 25, 2022

(See Gartner’s “ How DataOps Amplifies Data and Analytics Business Value ”). On the process side, DataOps is essentially an agile and unified approach to building data movements and transformation pipelines (think streaming and modern ETL). How can data users navigate and understand such a complex landscape predictably?

DataOps 52

DataOps SQL ML ML 52

Pickl AI

MARCH 19, 2025

It helps organisations understand their data better and make informed decisions. Apache Hive Apache Hive is a data warehouse tool that allows users to query and analyse large datasets stored in Hadoop. It simplifies data processing by providing an SQL-like interface for querying Big Data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52