DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Knowledge Hut

JANUARY 25, 2024

The specific methods and steps for data cleaning may vary depending on the dataset, but its importance remains constant in the data science workflow. Why Is Data Cleaning So Important? These issues can stem from various sources such as human error, data scraping, or the integration of data from multiple sources.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. Organizations need to automate various aspects of their data operations, including data integration, data quality, and data analytics.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Precisely

JANUARY 11, 2024

Automation is a key driver in achieving digital transformation outcomes like agility, speed, and data integrity. These efforts include adopting automation platforms with flexible, contingent workflow solutions that drive efficiencies and greater data integrity across multiple complex, data-intensive processes.

Technology 59

Technology Data Validation Raw Data Portfolio 59

Ascend.io

SEPTEMBER 19, 2023

In this article, we’ll delve into what is an automated ETL pipeline, explore its advantages over traditional ETL, and discuss the inherent benefits and characteristics that make it indispensable in the data engineering toolkit. ETL stands for: Extract: Retrieve raw data from various sources. The result?

ETL Tools 52

ETL Tools ETL Method Utilities Data Engineering 52

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Monte Carlo

MAY 31, 2023

In a data-driven world, data integrity is the law of the land. And if data integrity is the law, then a data quality integrity framework is the FBI, the FDA, and the IRS all rolled into one. Because if we can’t trust our data, we also can’t trust the products they’re creating.

Building 52

Building Data Validation Healthcare Data Integration 52

ProjectPro

AUGUST 21, 2015

Data that can be stored in traditional database systems in the form of rows and columns, for example, the online purchase transactions can be referred to as Structured Data. Data that can be stored only partially in traditional database systems, for example, data in XML records can be referred to as semi-structured data.

Hadoop 40

Hadoop Big Data Metadata Java 40