AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required.

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AWS ML ML Data Quality 93

Mlearning.ai

AUGUST 10, 2023

The project I did to land my business intelligence internship — CAR BRAND SEARCH ETL PROCESS WITH PYTHON, POSTGRESQL & POWER BI 1. Section 2: Explanation of the ETL diagram for the project. ETL ARCHITECTURE DIAGRAM ETL stands for Extract, Transform, Load. Figure 3: Car Brand search ETL diagram 2.1.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Power BI 52

IBM Journey to AI blog

MARCH 14, 2024

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

AWS 93

AWS Database ETL AI 93

AWS Machine Learning Blog

JUNE 25, 2024

To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device.

AWS 126

AWS Natural Language Processing Machine Learning Machine Learning 126

Becoming Human

JANUARY 23, 2023

Just like this in Data Science we have Data Analysis , Business Intelligence , Databases , Machine Learning , Deep Learning , Computer Vision , NLP Models , Data Architecture , Cloud & many things, and the combination of these technologies is called Data Science. So, it looks like magic but it’s not magic. If we talk about AI.

Data Science 105

Data Science Big Data Big Data Deep Learning 105

How to Learn Machine Learning

APRIL 26, 2025

Tools like Python (with pandas and NumPy), R, and ETL platforms like Apache NiFi or Talend are used for data preparation before analysis. AI Product Manager Manages AI-driven product development, requiring technical and business expertise. Business Intelligence (BI) Analyst Focuses on dash boarding, reporting, and KPI monitoring.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Alation

MAY 24, 2022

Alation has been leading the evolution of the data catalog to a platform for data intelligence. Higher data intelligence drives higher confidence in everything related to analytics and AI/ML. The Lineage & Dataflow API is a good example enabling customers to add ETL transformation logic to the lineage graph.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

phData

FEBRUARY 25, 2025

Over the years, businesses have increasingly turned to Snowflake AI Data Cloud for various use cases beyond just data analytics and business intelligence. Datavolo is more than just an ETL toolit provides functionality for Reverse ETL as well, enabling organizations to push data from Snowflake into other systems.

Data Pipeline 52

Data Pipeline ETL Data Engineering Data Engineering 52

IBM Journey to AI blog

AUGUST 4, 2023

Data mesh Another approach to data democratization uses a data mesh , a decentralized architecture that organizes data by a specific business domain. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Lakes 73

Data Lakes AI AI Data Governance 73

IBM Journey to AI blog

JANUARY 18, 2023

Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. This adds an additional ETL step, making the data even more stale. Data platform architecture has an interesting history. It was Datawarehouse.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

DagsHub

NOVEMBER 11, 2024

We use data-specific preprocessing and ML algorithms suited to each modality to filter out noise and inconsistencies in unstructured data. Embedding Generation: Bridging Data Types Embedding generation converts unstructured data into numerical vectors that ML models can understand. Tools like Unstructured.io

AI 52

AI AI Data Lakes Database 52

IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise. IBM watsonx.ai

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

DECEMBER 10, 2024

Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes. Feature Stores for AI/ML Feature stores play a vital role in operationalising Machine Learning (ML).

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

IBM Journey to AI blog

JULY 3, 2024

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.

Data Quality 45

Data Quality AI AI Database 45

databricks

JUNE 12, 2025

Sample Dataflow Graph Declarative APIs make ETL simpler and more maintainable Through years of working with real-world Spark users, we’ve seen common challenges emerge when building production pipelines: Too much time spent wiring together pipelines with “glue code” to handle incremental ingestion or deciding when to materialize datasets.

SQL 248

SQL Data Engineering Data Engineering Data Engineer 248

phData

JULY 18, 2023

This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data. Business-Focused Operation Model: Teams can shed countless hours of managing long-running and complex ETL pipelines that do not scale. Why Migrate to a Modern Data Stack?

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

databricks

JUNE 16, 2025

160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025. 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025.

Data Science 130

Data Science Business Intelligence Business Intelligence Artificial Intelligence 130

Flipboard

MAY 15, 2025

Data environments in data-driven organizations are changing to meet the growing demands for analytics , including business intelligence (BI) dashboarding, one-time querying, data science , machine learning (ML), and generative AI.

AWS 135

AWS SQL Data Lakes Data Warehouse 135

Flipboard

JUNE 16, 2025

Next Steps: Transition into data engineering (PySpark, ETL) or machine learning (TensorFlow, PyTorch). If undecided, begin with DSA and a versatile language like Python, which can later transition into ML or web development. Database Management : It is crucial to have knowledge of both relational (e.g., MongoDB, Cassandra) databases.

Python 50

Python AWS Machine Learning Machine Learning 50