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. This leads to faster insights and decision-making.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Monte Carlo

FEBRUARY 15, 2023

This article will define in simple terms what a data warehouse is, how it’s different from a database, fundamentals of how they work, and an overview of today’s most popular data warehouses. What is a data warehouse? Finally, where and how the data pipeline broke isn’t always obvious.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Ascend.io

SEPTEMBER 14, 2023

Here are some examples of how Python can be applied to various facets of data engineering: Data Collection Web scraping has become an accessible task thanks to Python libraries like Beautiful Soup and Scrapy, empowering engineers to easily gather data from web pages. csv') data_excel = pd.read_excel('data2.xlsx')

Data Engineer 52

Data Engineer Data Engineering Python Engineering 52

phData: Data Engineering

JANUARY 3, 2022

The role of a data engineer is going to vary depending on the particular needs of your organization. It’s the role of a data engineer to store, extract, transform, load, aggregate, and validate data. This involves: Building data pipelines and efficiently storing data for tools that need to query the data.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

Rockset

DECEMBER 17, 2020

Overview of the Customer 360 App Our app will make use of real-time data on customer orders and events. We’ll use Rockset to get data from different sources and run analytical queries that power our app in Retool. From there, we’ll create a data API for the SQL query we write in Rockset.

Building 40

Building SQL Aggregated Data Database 40

ProjectPro

JANUARY 25, 2022

PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. Another reason to use PySpark is that it has the benefit of being able to scale to far more giant data sets compared to the Python Pandas library.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. to accumulate data over a given period for better analysis.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

ProjectPro

APRIL 20, 2017

There are various kinds of hadoop projects that professionals can choose to work on which can be around data collection and aggregation, data processing, data transformation or visualization. Extracting data from APIs using Python. Uploading the data on HDFS. Utilizing PySpark for reading data.

Hadoop 40

Hadoop Big Data Coding Project 40