Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 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 Engineering 52

Data Engineering Data Engineer Python Engineering 52

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. How small file problems in streaming can be resolved using a NoSQL database.

Hadoop 40

Hadoop Big Data Coding Project 40

Rockset

OCTOBER 26, 2022

Also, DynamoDB, as a NoSQL database, doesn’t support SQL commands such as JOINING multiple tables. One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. That’s where DynamoDB’s analytical limitations reared their ugly heads.

SQL 52

SQL NoSQL Database Aggregated Data 52

AltexSoft

SEPTEMBER 21, 2023

In this edition of “The Good and The Bad” series, we’ll dig deep into Elasticsearch — breaking down its functionalities, advantages, and limitations to help you decide if it’s the right tool for your data-driven aspirations. Fluentd is a data collector and a lighter-weight alternative to Logstash. What is Elasticsearch?

Engineering 52

Engineering NoSQL Programming Language Java 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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40