Data Science Dojo
JULY 6, 2023
Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing.
Pickl AI
JULY 3, 2023
What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50
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IBM Journey to AI blog
JANUARY 5, 2023
The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?
Mlearning.ai
FEBRUARY 16, 2023
Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.
IBM Journey to AI blog
SEPTEMBER 19, 2023
Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes.
IBM Journey to AI blog
OCTOBER 16, 2023
It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads.
phData
AUGUST 10, 2023
The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. The most important reason for using DBT in Data Vault 2.0 Automate and prioritize Test Driven Development (TDD) for data vault projects.
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