Datasets ETL Tools NoSQL 
Knowledge Hut
DECEMBER 26, 2023
These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. The dataset can be either structured or unstructured or both. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout.
AltexSoft
OCTOBER 30, 2021
Data scientist’s responsibilities — Datasets and Models. Machine learning algorithms are designed to solve specific problems, though other conditions factor in the choice: the dataset size, the training time that you have, number of features, etc. Distinction between data scientists and engineers is similar. Let’s explore it.
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Lyft Engineering
OCTOBER 3, 2023
Semantic Correctness — The core.rider_events derived dataset shows a drastic increase in today’s cancels volume, caused by a bug in the origin web service creating the event. This is useful because these users are often not familiar with ETL tooling. As such, DynamoDB was a natural choice as a NoSQL key-value store.
Knowledge Hut
NOVEMBER 3, 2023
MongoDB is a NoSQL database that’s been making rounds in the data science community. MongoDB is used for data science, meaning that we utilize the capabilities of this NoSQL database system as part of our data analysis and data modeling processes, which fall under the realm of data science. What is MongoDB for Data Science?
Edureka
AUGUST 29, 2024
Their roles are expounded below: Acquire Datasets: It is about acquiring datasets that are focused on defined business objectives to drive out relevant insight. Databases: Knowledgeable about SQL and NoSQL databases. Data Warehousing: Experience in using tools like Amazon Redshift, Google BigQuery, or Snowflake.
Rockset
JANUARY 28, 2022
Seesaw was able to scale up its main database, an Amazon DynamoDB cloud-based service optimized for large datasets. However, Seesaw’s DynamoDB database stored the data in its own NoSQL format that made it easy to build applications, just not analytical ones. Storing all of that data was not a problem.
Monte Carlo
NOVEMBER 20, 2023
A data migration is the process where old datasets, perhaps resting in outdated systems, are transferred to newer, more efficient ones. And the larger your datasets, the more meticulous planning you have to do. What makes data migrations complex? Sure, you’re moving data from point A to point B, but the reality is far more nuanced.
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