Ascend.io

NOVEMBER 21, 2023

The transformation is governed by predefined rules that dictate how the data should be altered to fit the requirements of the target data store. This process can encompass a wide range of activities, each aiming to enhance the data’s usability and relevance. Read More: Zero ETL: What’s Behind the Hype?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Knowledge Hut

JANUARY 25, 2024

Let's dive into the top data cleaning techniques and best practices for the future – no mess, no fuss, just pure data goodness! What is Data Cleaning? It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data. Why Is Data Cleaning So Important?

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Knowledge Hut

APRIL 19, 2023

The process of merging and summarizing data from various sources in order to generate insightful conclusions is known as data aggregation. The purpose of data aggregation is to make it easier to analyze and interpret large amounts of data. This can be done manually or with a data cleansing tool.

Process 59

Process Data Mining Aggregated Data Portfolio 59

Knowledge Hut

OCTOBER 13, 2023

Aggregate Data: If you don't need granularity, consider aggregating data before loading it into Power BI to reduce the volume of data. Sort and Filter Early: Apply sorting and filtering in your queries as early as possible to reduce the amount of data transferred and processed.

BI 52

BI Business Analyst Datasets Raw Data 52

AltexSoft

DECEMBER 23, 2022

Also known as data scrubbing or data cleaning, it is the process of identifying and correcting or removing inaccuracies and inconsistencies in data. Data cleansing is often necessary because data can become dirty or corrupted due to errors, duplications, or other issues. Aggregation.

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

AUGUST 24, 2021

This project is an opportunity for data enthusiasts to engage in the information produced and used by the New York City government. to accumulate data over a given period for better analysis. Create an external table in Hive, perform data cleansing and transformation operations, and store the data in a target table.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52