ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

FEBRUARY 20, 2020

It’s no longer enough to build the data warehouse. Dave Wells, analyst with the Eckerson Group suggests that realizing the promise of the data warehouse requires a paradigm shift in the way we think about data along with a change in how we access and use it.

Data Warehouse 52

Data Warehouse Data Lakes Hadoop Data Preparation 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 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 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

FEBRUARY 13, 2020

By leveraging Google-like smart search to find data assets; using automation and self-learning instead of burdening people with the need to manually update metadata in multiple places; and ensuring that metadata is maintained by the whole data community and is not dependent on a centralized IT team.

Machine Learning 52

Machine Learning Machine Learning Analytics Analytics 52

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 30, 2024

ETL (Extract, Transform, Load) Processes Apache NiFi can streamline ETL processes by extracting data from multiple sources, transforming it into the desired format, and loading it into target systems such as data warehouses or databases. Its visual interface allows users to design complex ETL workflows with ease.

ETL 52

ETL Data Lakes Big Data Big Data 52

Alation

JANUARY 25, 2022

This is a key component of active data governance. These capabilities are also key for a robust data fabric. Another key nuance of a data fabric is that it captures social metadata. Social metadata captures the associations that people create with the data they produce and consume. The Power of Social Metadata.

DataOps 52

DataOps SQL ML ML 52

IBM Journey to AI blog

JULY 5, 2023

The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines. Data governance remains an unexplored frontier for this technology.

Data Lakes 78

Data Lakes Data Warehouse Data Governance Analytics 78