Pickl AI

NOVEMBER 4, 2024

Additionally, attending webinars and local meetups can significantly expand your knowledge and connections. Webinars often feature industry experts who share practical insights and experiences. Additionally, learn about data storage options like Hadoop and NoSQL databases to handle large datasets.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Dataconomy

JULY 25, 2023

Big data tools : Familiarity with big data tools like Hadoop, Spark, and NoSQL databases is advantageous for handling large-scale datasets. Participate in online courses, webinars, and workshops to keep your skills relevant and in demand.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Alation

MARCH 29, 2021

Alation crawls and indexes data assets stored across disparate repositories, including cloud data lakes, databases, Hadoop files, and data visualization tools. For expert advice on cloud migration strategy, join us for this webinar, Expert Panel: Pain Points of Moving Data to the Cloud and Strategies for Success.

Machine Learning 59

Machine Learning Machine Learning Data Lakes Hadoop 59

Pickl AI

MARCH 10, 2023

Some of the tools used by Data Science in 2023 include statistical analysis system (SAS), Apache, Hadoop, and Tableau. Additionally, you should attend conferences and events like webinars and learn from your peers and experts. It contains data clustering, classification, anomaly detection and time-series forecasting.

Data Scientist 52

Data Scientist Data Science Apache Hadoop Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Big Data Tools Integration Big data tools like Apache Spark and Hadoop are vital for managing and processing massive datasets. With its distributed storage and processing capabilities, Hadoop helps store vast amounts of data across multiple machines, ensuring the efficient handling of unstructured data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Alation

JANUARY 25, 2022

Webinar: Five Must-Haves for a Data Catalog. These approaches extend the continuum of enterprise data warehouses, federated data marts, big data (Hadoop), and virtualization on top of distributed cloud file storage. But how do you go about selecting the right data catalog?

DataOps 52

DataOps SQL ML ML 52