Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. It provides a scalable and fault-tolerant ecosystem for big data processing.

Data Engineering 195

Data Engineering Data Engineering Data Engineer Data Engineering 195

Pickl AI

NOVEMBER 4, 2024

It enables reporting and Data Analysis and provides a historical data record that can be used for decision-making. Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Precisely

MARCH 9, 2023

Raw Data Data warehouses emerged several decades ago as a means of combining, harmonizing, and preprocessing data in preparation for advanced analytics. A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

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 Engineering Data Engineering Data Engineer 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

It allows unstructured data to be moved and processed easily between systems. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52