ProjectPro

FEBRUARY 25, 2022

Data Ingestion Data Processing Data Splitting Model Training Model Evaluation Model Deployment Monitoring Model Performance Machine Learning Pipeline Tools Machine Learning Pipeline Deployment on Different Platforms FAQs What tools exist for managing data science and machine learning pipelines?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Knowledge Hut

JUNE 26, 2023

From analysts to Big Data Engineers, everyone in the field of data science has been discussing data engineering. When constructing a data engineering project, you should prioritize the following areas: Multiple sources of data (APIs, websites, CSVs, JSON, etc.) Source Code: Anomaly Detection in Cloud Servers 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Monte Carlo

FEBRUARY 20, 2024

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

ProjectPro

JANUARY 19, 2022

Independently create data-driven solutions that are accurate and informative. Interact with the data scientists team and assist them in providing suitable datasets for analysis. Leverage various big data engineering tools and cloud service providing platforms to create data extractions and storage pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As a certified Azure Data Engineer, you have the skills and expertise to design, implement and manage complex data storage and processing solutions on the Azure cloud platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. In Snowflake, there are three different storage layers available, Database, Stage, and Cloud Storage.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Data pipeline architecture typically consisted of hardcoded pipelines that cleaned, normalized, and transformed the data prior to loading into a database using an ETL pattern. Data could now be extracted and loaded prior to being transformed for its ultimate use.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

FEBRUARY 8, 2023

For e.g., Finaccel, a leading tech company in Indonesia, leverages AWS Glue to easily load, process, and transform their enterprise data for further processing. Another leading European company, Claranet, has adopted Glue to migrate their data load from their existing on-premise solution to the cloud. How Does AWS Glue Work?

AWS 98

AWS Scala Metadata Data Lake 98

phData: Data Engineering

JANUARY 3, 2022

This involves: Building data pipelines and efficiently storing data for tools that need to query the data. Analyzing the data, ensuring it adheres to data governance rules and regulations. Understanding the pros and cons of data storage and query options.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. Big Data analytics processes and tools. Data ingestion.

Big Data 78

Big Data Data Analytics IT NoSQL 78

U-Next

OCTOBER 27, 2022

Cloud computing is the term used to describe internet data storage and access. It doesn’t store any data on your computer’s hard drive and allows users to access data from faraway servers. Software experts overseeing a company’s cloud computing architecture are AWS solutions architects.

AWS 40

AWS Amazon Web Services Cloud Computing Cloud 40

ProjectPro

MAY 31, 2021

This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow. Snowflake Real-Time Data Warehouse Project for Beginners Snowflake provides a cloud-based analytics and data storage service called "data warehouse-as-a-service."

Big Data 52

Big Data Coding Project Hadoop 52

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Below, we share what the “basic” data platform looks like and list some hot tools in each space (you’re likely using several of them): The modern data platform is composed of five critical foundation layers. Data Storage and Processing The first layer?

Building 52

Building BI Data Lake Data Governance 52

Knowledge Hut

DECEMBER 13, 2023

They are experienced in practically every industry and have experience with blockchain, cloud, SAP, and AI solutions. Tech Mahindra Tech Mahindra is a service-based company with a data-driven focus. SAPC The HANA-in memory SQL server is the SAPC's primary big data tool; however, it also offers several analytics tools.

Big Data 52

Big Data Consulting Hadoop Amazon Web Services 52

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. Storage, compute, hardware, and platform maintenance (and all its associated costs) are handled for you.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52