Smart Data Collective

APRIL 2, 2019

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

Data Lakes 83

Data Lakes Big Data Big Data Data Scientist 83

Data Science Dojo

JULY 6, 2023

It integrates seamlessly with other AWS services and supports various data integration and transformation workflows. Google BigQuery: Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics. Airflow An open-source platform for building and scheduling data pipelines.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Text analytics is crucial for sentiment analysis, content categorization, and identifying emerging trends. Ensure that data is clean, consistent, and up-to-date.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Data Warehousing concepts and knowledge should be strong.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.

AWS 132

AWS ML ML Analytics 132

Precisely

OCTOBER 16, 2023

Rapid advancements in digital technologies are transforming cloud-based computing and cloud analytics. Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72