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

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 Aggregated Data SQL Database 40

Confluent

FEBRUARY 6, 2019

While all these solutions help data scientists, data engineers and production engineers to work better together, there are underlying challenges within the hidden debts: Data collection (i.e., Apache Kafka and KSQL for data scientists and data engineers. integration) and preprocessing need to run at scale.

Machine Learning 107

Machine Learning Python Kafka Java 107

AltexSoft

SEPTEMBER 21, 2023

Logstash is a server-side data processing pipeline that ingests data from multiple sources, transforms it, and then sends it to Elasticsearch for indexing. Fluentd is a data collector and a lighter-weight alternative to Logstash. It is designed to unify data collection and consumption for better use and understanding.

Engineering 52

Engineering NoSQL Programming Language Java 52

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

JUNE 28, 2016

Kafka is extensively being used across industries for general – purpose messaging system where high availability and real time data integration and analytics are of utmost importance. It provides a scalable and reliable messaging system for cloud based environment for asynchronous messaging distribution.

Kafka 40

Kafka Systems Hadoop Big Data 40

phData: Data Engineering

JANUARY 3, 2022

This likely requires you to aggregate data from your ERP system, your supply chain system, potentially third-party vendors, and data around your internal business structure. Data engineers are responsible for putting processes in place to ensure that data pipelines, databases, and data warehouses meet these metrics.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer 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 Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

DoorDash Engineering

OCTOBER 17, 2023

They subsequently adjust the experiment’s start date so that it does not include metric data collected prior to the bug fix. Experiment exposures are one of our highest volume events. On a typical day, our platform produces between 80 billion and 110 billion exposure events.

Education 87

Education Kafka Algorithm Data Warehouse 87

ProjectPro

OCTOBER 29, 2021

Real-world databases are often incredibly noisy, brimming with missing and inconsistent data and other issues that are often amplified by their enormous size and heterogeneous sources of origin caused by what seems to be an unending pursuit to amass more data. Nonparametric.

Data Mining 52

Data Mining Datasets Machine Learning Metadata 52