IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Data scientists will typically perform data analytics when collecting, cleaning and evaluating data.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

ODSC - Open Data Science

APRIL 3, 2023

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. As you see, there are a number of reporting platforms as expected.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

AWS Machine Learning Blog

MARCH 8, 2023

Data collection and ingestion The data collection and ingestion layer connects to all upstream data sources and loads the data into the data lake. One function to consolidate and prepare the data for training. The data can also be made available via API to downstream business applications.

AWS 92

AWS Machine Learning Machine Learning Analytics 92

phData

DECEMBER 4, 2023

The software you might use OAuth with includes: Tableau Power BI Sigma Computing If so, you will need an OAuth provider like Okta, Microsoft Azure AD, Ping Identity PingFederate, or a Custom OAuth 2.0 Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts!

Clustering 98

Clustering Database SQL Data Pipeline 98

Pickl AI

AUGUST 9, 2024

NoSQL Databases These databases, such as MongoDB, Cassandra, and HBase, are designed to handle unstructured and semi-structured data, providing flexibility and scalability for modern applications. Understanding the differences between SQL and NoSQL databases is crucial for students. js for creating interactive visualisations.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

SEPTEMBER 5, 2024

It offers a wide range of cloud services, including: Compute Power: Scalable virtual machines and container services for running applications. Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Data Science Blog

JULY 20, 2024

Automatisierung: Erstellt SQL-Code, DACPAC-Dateien, SSIS-Pakete, Data Factory-ARM-Vorlagen und XMLA-Dateien. Vielfältige Unterstützung: Kompatibel mit verschiedenen Datenbankmanagementsystemen wie MS SQL Server und Azure Synapse Analytics. Data Lakes: Unterstützt MS Azure Blob Storage.

Azure 130

Azure SQL Power BI Data Lakes 130