Knowledge Hut

JANUARY 29, 2024

In contrast, ETL is primarily employed by DW/ETL developers responsible for data integration between source systems and reporting layers. Data Structure: Data wrangling deals with varied and complex data sets, which may include unstructured or semi-structured data. Frequently Asked Questions (FAQs) 1.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Monte Carlo

OCTOBER 10, 2024

Attention to Detail : Critical for identifying data anomalies. Tools : Familiarity with data validation tools, data wrangling tools like Pandas , and platforms such as AWS , Google Cloud , or Azure. Data observability tools: Monte Carlo ETL Tools : Extract, Transform, Load (e.g., Informatica , Talend ).

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Knowledge Hut

JULY 26, 2023

This velocity aspect is particularly relevant in applications such as social media analytics, financial trading, and sensor data processing. Variety: Variety represents the diverse range of data types and formats encountered in Big Data. Handling this variety of data requires flexible data storage and processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Monte Carlo

MAY 28, 2024

Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. In batch processing, this occurs at scheduled intervals, whereas real-time processing involves continuous loading, maintaining up-to-date data availability. Used for identifying and cataloging data sources.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Precisely

APRIL 30, 2024

With a complex data validation process, for example, an RPA bot might struggle to identify and handle unexpected errors. These include: Structured data dependence: RPA solutions thrive on well-organized, predictable data. It struggles with unstructured data like emails, scanned documents, or free-form text.

Unstructured Data 59

Unstructured Data Government Data Validation Programming 59

RandomTrees

SEPTEMBER 6, 2020

A combination of structured and semi structured data can be used for analysis and loaded into the cloud database without the need of transforming into a fixed relational scheme first. The key features include: Rapid migration of data from SAP BW and HANA. Automated data cleansing and predefined data validation.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Striim

JANUARY 4, 2023

According to them, a data contract implementation consists of the following components, as depicted below: Defining data contracts as code using open-source projects (e.g. Apache Avro) to serialize and deserialize structured data.

PostgreSQL 52

PostgreSQL Data Warehouse Data Data Lake 52

Rockset

FEBRUARY 24, 2023

Strong schema support : Avro has a well-defined schema that allows for type safety and strong data validation. Sample use case: Avro is a good choice for big data platforms that need to process and analyze large volumes of log data.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Databand.ai

NOVEMBER 4, 2022

If the data includes an old record or an incorrect value, then it’s not accurate and can lead to faulty decision-making. Data content: Are there significant changes in the data profile? Data validation: Does the data conform to how it’s being used?

Data 52

Data Data Pipeline Retail Metadata 52

Ascend.io

AUGUST 31, 2023

Stepwise Transformation: Structuring data transformation in sequential steps provides clarity and control over sophisticated data operations such as business validation, data normalization, and analytics functions.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Pipeline 52

Monte Carlo

JULY 10, 2023

Incomplete data from external sources When you ingest data from an external source, you lack a certain amount of control over how data is structured and made available. Different sources may structure data inconsistently, which can lead to missing values within your data sets.

Data Collection 40

Data Collection Data Governance Government Data 40

Knowledge Hut

OCTOBER 30, 2023

Data Analysis: Perform basic data analysis and calculations using DAX functions under the guidance of senior team members. Data Integration: Assist in integrating data from multiple sources into Power BI, ensuring data consistency and accuracy. Ensure compliance with data protection regulations.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Ascend.io

JUNE 8, 2023

Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

JANUARY 25, 2023

The contracts themselves should be created using well-established protocols for serializing and deserializing structured data such as Google’s Protocol Buffers (protobuf), Apache Avro, or even JSON. They provide common data checks and a way to write custom tests within your dbt project. Consistency in your tech stack.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

ProjectPro

FEBRUARY 16, 2023

Data validations or data type checks can be performed using SQL, while duplicates, foreign key constraints, and NULL checks can all be identified using ETL solutions. Data processing tasks containing SQL-based data transformations can be conducted utilizing Hadoop or Spark executors by ETL solutions.

Data Engineer 52

Data Engineer Data Engineering SQL Engineering 52

ProjectPro

JANUARY 31, 2023

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Ascend.io

JULY 19, 2023

To ensure consistency in the data product definitions across domains, these guidelines should at least cover: Metadata standards: Define a standard set of metadata to accompany every data product. This might include information about the data source, the type of data, the date of creation, and any relevant context or description.

Data Governance 40

Data Governance Government Metadata Data 40

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

Data Engineering Weekly

FEBRUARY 12, 2023

It’s true Big Data is dead, but we can’t deny it is a result of collective advancement in data processing techniques. link] Dropbox: Balancing quality and coverage with our data validation framework Data Testing should be part of the data creation lifecycle; it is not a standalone process.

Data Engineer 52

Data Engineer Data Engineering Engineering Hadoop 52