ODSC - Open Data Science

SEPTEMBER 27, 2023

Data Warehouses and Relational Databases It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics. Schema Enforcement: Data warehouses use a “schema-on-write” approach. Interested in attending an ODSC event?

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

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

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 Engineer Data Engineering Data Engineering 52

Pickl AI

MARCH 19, 2025

Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data. It supports batch processing and real-time streaming, making it a go-to tool for data engineers working with large datasets. Apache Kafka Apache Kafka is a distributed event streaming platform used for real-time data processing.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

JUNE 19, 2023

As the name suggests, real-time operating systems (RTOS) handle real-time applications that undertake data and event processing under a strict deadline. It is also important to establish data quality standards and strict access controls. Moreover, RTOS is built to be scalable and flexible.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Precisely

MARCH 12, 2024

Better data quality. Customer data quality decays quickly. By enriching your data with information from trusted sources, you can verify information and automatically update it when appropriate. How does data enrichment work? What is the difference between data enrichment and data quality?

Data Quality 52

Data Quality ETL Analytics Analytics 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. Then there’s data quality, and then explainability. Arize AI The third pillar is data quality. And data quality is defined as data issues such as missing data or invalid data, high cardinality data, or duplicated data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. If you aren’t aware already, let’s introduce the concept of ETL. Redshift, S3, and so on.

AWS 52

AWS ETL ML ML 52

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

Alation

JUNE 23, 2023

That’s going to be the view at the highly anticipated gathering of the global data, analytics, and AI community — Databricks Data + AI Summit — when it makes its grand return to San Francisco from June 26–29. We’re looking forward to seeing you there! When: Thursday, June 29, at 11:30 p.m.

AI 52

AI AI ETL Data Quality 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. Then there’s data quality, and then explainability. Arize AI The third pillar is data quality. And data quality is defined as data issues such as missing data or invalid data, high cardinality data, or duplicated data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. Then there’s data quality, and then explainability. Arize AI The third pillar is data quality. And data quality is defined as data issues such as missing data or invalid data, high cardinality data, or duplicated data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

phData

OCTOBER 17, 2024

Apache Airflow Airflow is an open-source ETL software that is very useful when paired with Snowflake. The DAGs can then be scheduled to run at specific intervals or triggered when an event occurs. The Data Source Tool can automate scanning DDL and profiling tables between source and target, comparing them, and then reporting findings.

AI 52

AI AI SQL Data Quality 52

Tableau

AUGUST 21, 2023

Catalog Enhanced data trust, visibility, and discoverability Tableau Catalog automatically catalogs all your data assets and sources into one central list and provides metadata in context for fast data discovery. Included with Data Management.

Tableau 97

Tableau Database Analytics Analytics 97

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. It truly is an all-in-one data lake solution. Interested in attending an ODSC event?

Data Lakes 52

Data Lakes Clustering Big Data Big Data 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

Pickl AI

AUGUST 9, 2024

Data Integration Tools Technologies such as Apache NiFi and Talend help in the seamless integration of data from various sources into a unified system for analysis. Understanding ETL (Extract, Transform, Load) processes is vital for students. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DagsHub

OCTOBER 23, 2024

They assist in efficiently managing and processing data from multiple sources, ensuring smooth integration and analysis across diverse formats. Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Transform the unstructured data into a more structured format.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JUNE 7, 2023

You don’t need massive data sets because “data quality scales better than data size.” ” Small models with good data are better than massive models because “in the long run, the best models are the ones which can be iterated upon quickly.”

AI 59

AI AI Machine Learning Machine Learning 59

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

SQL 141

SQL Data Analyst Data Warehouse AWS 141

phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Women in Big Data

JANUARY 25, 2025

Together with the Hertie School , we co-hosted an inspiring event, Empowering in Data & Governance. The event was opened by Aliya Boranbayeva , representing Women in Big Data Berlin and the Hertie School Data Science Lab , alongside Matthew Poet , representing the Hertie School.

Data Governance 52

Data Governance Big Data Big Data Data Quality 52