Monte Carlo

JUNE 2, 2024

Managing cloud-based data services, cost optimization, and scaling are key responsibilities, and these trends are likely to grow along with the future of data governance. Data Pipeline Tools: Familiarity with tools such as Apache Kafka (mentioned in 71% of job postings) and Apache Spark (66%) is vital.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

Managing cloud-based data services, cost optimization, and scaling are key responsibilities, and these trends are likely to grow along with the future of data governance. Data Pipeline Tools: Familiarity with tools such as Apache Kafka (mentioned in 71% of job postings) and Apache Spark (66%) is vital.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Edureka

JULY 16, 2024

Machine Learning in AWS SageMaker Machine learning in AWS SageMaker involves steps facilitated by various tools and services within the platform: Data Preparation: SageMaker comprises tools for labeling the data and data and feature transformation. What are SageMaker Use Cases?

AWS 52

AWS Algorithm Machine Learning Amazon Web Services 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

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

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 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. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Notice how Snowflake dutifully avoids (what may be a false) dichotomy by simply calling themselves a “data cloud.”

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

APRIL 25, 2023

It is widely used by data engineers for building scalable and reliable data processing systems. Hadoop provides tools for data storage, processing, and analysis, including Hadoop Distributed File System (HDFS) and MapReduce. It can add more processing power and storage as the data grows.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 52

ProjectPro

FEBRUARY 8, 2023

Smooth Integration with other AWS tools AWS Glue is relatively simple to integrate with data sources and targets like Amazon Kinesis, Amazon Redshift, Amazon S3, and Amazon MSK. It is also compatible with other popular data storage that may be deployed on Amazon EC2 instances.

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

JANUARY 19, 2022

Cloud Services Providers Platforms As companies are gradually becoming more inclined towards investing in cloud computing for storing their data instead of bulky hardware systems, engineers who can work on cloud computing tools are in demand. Best suited for those looking for Platform-as-a-service (PaaS) provider.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 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.) Master data processing methods.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

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

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 Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

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. Data ingestion capability . Introduction . Listening to and comprehending business needs .

AWS 40

AWS Amazon Web Services Cloud Computing Cloud 40

ProjectPro

MAY 31, 2021

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes: people_positive_cases_count county_name case_type data_source Language Used: Python 3.7 What are the main components of a big data architecture?

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

Tech Mahindra Tech Mahindra is a service-based company with a data-driven focus. The complex data activities, such as data ingestion, unification, structuring, cleaning, validating, and transforming, are made simpler by its self-service. It also makes it easier to load the data into destination databases.

Big Data 52

Big Data Consulting Hadoop Amazon Web Services 52

phData: Data Engineering

JULY 18, 2023

Zero Copy Cloning: Create multiple ‘copies’ of tables, schemas, or databases without actually copying the data. This noticeably saves time on copying and drastically reduces data storage costs. Snowflake allows its users to interface with the software without worrying about the infrastructure it runs or how to install it.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52