Smart Data Collective

AUGUST 17, 2022

Kafka And ETL Processing: You might be using Apache Kafka for high-performance data pipelines, stream various analytics data, or run company critical assets using Kafka, but did you know that you can also use Kafka clusters to move data between multiple systems. 5 Key Comparisons in Different Apache Kafka Architectures.

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

ODSC - Open Data Science

MARCH 19, 2025

As AI and data engineering continue to evolve at an unprecedented pace, the challenge isnt just building advanced modelsits integrating them efficiently, securely, and at scale. This session explores open-source tools and techniques for transforming unstructured documents into structured formats like JSON and Markdown.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

DECEMBER 4, 2024

Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines. The following diagram illustrates the conceptual architecture of an AI assistant with Amazon Bedrock IDE.

AWS 106

AWS AI AI SQL 106

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

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

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. Track models and drive transparent processes.

AI 88

AI AI Data Warehouse ML 88

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 Implement Data Lineage and Traceability Path: Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Pickl AI

OCTOBER 10, 2024

Inconsistent or unstructured data can lead to faulty insights, so transformation helps standardise data, ensuring it aligns with the requirements of Analytics, Machine Learning , or Business Intelligence tools. This makes drawing actionable insights, spotting patterns, and making data-driven decisions easier.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data. This allows for a high degree of transparency and auditability.

AI 93

AI AI Data Scientist Data Governance 93

Pickl AI

APRIL 9, 2025

This stage involves optimizing the data for querying and analysis. This process ensures that organizations can consolidate disparate data sources into a unified repository for analytics and reporting, thereby enhancing business intelligence. Security : Does the tool ensure data privacy and security during the ETL process?

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

phData

MAY 25, 2023

How to Optimize Power BI and Snowflake for Advanced Analytics Spencer Baucke May 25, 2023 The world of business intelligence and data modernization has never been more competitive than it is today. Microsoft Power BI has been the leader in the analytics and business intelligence platforms category for several years running.

Power BI 84

Power BI Analytics Analytics Azure 84

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.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

databricks

JUNE 12, 2025

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

SQL 251

SQL Data Engineering Data Engineering Data Engineer 251

Precisely

APRIL 3, 2023

It simply wasn’t practical to adopt an approach in which all of an organization’s data would be made available in one central location, for all-purpose business analytics. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Analytics Analytics Database 52

Pickl AI

FEBRUARY 8, 2023

Data Manipulation The process through which you can change the data according to your project requirement for further data analysis is known as Data Manipulation. The entire process involves cleaning, Merging and changing the data format. This data can help in building the project pipeline.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. Edge computing can be decentralized from on-premises, cellular, data centers, or the cloud. Problems arise when data sources are semantically incompatible. Record-keeping in a data catalog is key.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

AI 74

AI AI Machine Learning Machine Learning 74

DagsHub

AUGUST 23, 2024

TDWI Data Quality Framework This framework , developed by the Data Warehousing Institute, focuses on practical methodologies and tools that address managing data quality across various stages of the data lifecycle, including data integration, cleaning, and validation.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

In today’s digital world, data is king. Organizations that can capture, store, format, and analyze data and apply the business intelligence gained through that analysis to their products or services can enjoy significant competitive advantages. But, the amount of data companies must manage is growing at a staggering rate.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. It should also enable easy sharing of insights across the organization.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52