Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Database 130

Database Data Visualization Big Data Big Data 130

IBM Journey to AI blog

OCTOBER 22, 2024

Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models. This process is known as data integration , one of the key components to improving the usability of data for AI and other use cases, such as business intelligence (BI) and analytics.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

AWS Machine Learning Blog

DECEMBER 4, 2024

Its sales analysts face a daily challenge: they need to make data-driven decisions but are overwhelmed by the volume of available information. They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels.

AWS 107

AWS AI AI SQL 107

Tableau

MAY 19, 2021

Fortunately, a modern data stack (MDS) using Fivetran, Snowflake, and Tableau makes it easier to pull data from new and various systems, combine it into a single source of truth, and derive fast, actionable insights. What is a modern data stack? Transparency .

Tableau 91

Tableau Cloud Data Data Pipeline Analytics 91

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

AWS Machine Learning Blog

AUGUST 8, 2024

RAG optimizes language model outputs by extending the models’ capabilities to specific domains or an organization’s internal data for tailored responses. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.

SQL 128

SQL Data Lakes Data Analyst AWS 128

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

FEBRUARY 20, 2020

Self-service analytics tools have been democratizing data-driven decision making, but also increasing the risk of inaccurate analysis and misinterpretation. A “catalog-first” approach to business intelligence enables both empowerment and accuracy; and Alation has long enabled this combination over Tableau.

Data Governance 52

Data Governance Tableau Hadoop Data Pipeline 52

Pickl AI

APRIL 9, 2025

The importance of ETL tools is underscored by their ability to handle diverse data sources, from relational databases to cloud-based services. This capability allows organizations to consolidate disparate data into a unified repository for analytics and reporting, providing insights that can drive strategic decisions.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

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. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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. Furthermore, Datavolo provides a graphical UI that simplifies defining data pipelines.

Data Pipeline 52

Data Pipeline ETL Data Engineering Data Engineer 52

phData

JANUARY 5, 2023

There’s a multitude of different reasons why an organization may consider a business intelligence (BI) platform migration. On-Premises to The Cloud This type of migration involves moving an organization’s BI platform from an on-premises environment (such as a local server or data center) to a cloud-based environment.

Analytics 52

Analytics Analytics Tableau Database 52

Tableau

MAY 19, 2021

Fortunately, a modern data stack (MDS) using Fivetran, Snowflake, and Tableau makes it easier to pull data from new and various systems, combine it into a single source of truth, and derive fast, actionable insights. What is a modern data stack? Transparency .

Tableau 52

Tableau Cloud Data Data Pipeline Analytics 52

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Its PostgreSQL foundation ensures compatibility with most SQL clients.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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. Snowflake data cloud provides IP whitelisting to restrict access to data to authorized users.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

Pickl AI

JULY 25, 2024

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. It allows for intuitive data exploration and reporting, supports complex queries, and enables users to derive meaningful insights quickly.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 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

phData

NOVEMBER 9, 2023

A cloud data warehouse is designed to combine a concept that every organization knows, namely a data warehouse, and optimizes the components of it, for the cloud. What is a Data Lake? A Data Lake is a location to store raw data that is in any format that an organization may produce or collect.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

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?

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

Alation

MARCH 31, 2022

In the breakneck world of data, which I have been privy to since the mid 1990s, business intelligence remains one of the most enduring terms. The writer Richard Millar Devens used “business intelligence” to describe how a banker had the foresight to gather and act on information thus getting the jump on his competition.

Business Intelligence 52

Business Intelligence Business Intelligence Data Warehouse Data Scientist 52

IBM Journey to AI blog

OCTOBER 16, 2023

This type of next-generation data store combines a data lake’s flexibility with a data warehouse’s performance and lets you scale AI workloads no matter where they reside. It allows for automation and integrations with existing databases and provides tools that permit a simplified setup and user experience.

AI 88

AI AI Data Warehouse ML 88

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

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

Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Manage data with a seamless, consistent design experience – no need for complex coding or highly technical skills. What does all this mean for your business?

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

phData

SEPTEMBER 25, 2024

Under this category, tools with pre-built connectors for popular data sources and visual tools for data transformation are better choices. Integration: How well does the tool integrate with your existing infrastructure, databases, cloud platforms, and analytics tools? What is Fivetran?

ETL 52

ETL Database Data Warehouse Analytics 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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

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 is its ability to define and use macros.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

IBM Journey to AI blog

DECEMBER 16, 2022

Because Alex can use a data catalog to search all data assets across the company, she has access to the most relevant and up-to-date information. She can search structured or unstructured data, visualizations and dashboards, machine learning models, and database connections.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

AWS Machine Learning Blog

AUGUST 12, 2024

Separately, the company uses AWS data services, such as Amazon Simple Storage Service (Amazon S3), to store data related to patients, such as patient information, device ownership details, and clinical telemetry data obtained from the wearables. The approach is shown in the following diagram.

ML 124

ML ML AWS AI 124

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

IBM Journey to AI blog

JULY 17, 2023

Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent. 1 Watsonx.data offers built-in governance and automation to get to trusted insights within minutes, and integrations with existing databases and tools to simplify setup and user experience.

AI 74

AI AI Machine Learning Machine Learning 74

IBM Journey to AI blog

JULY 1, 2024

Whenever anyone talks about data lineage and how to achieve it, the spotlight tends to shine on automation. This is expected, as automating the process of calculating and establishing lineage is crucial to understanding and maintaining a trustworthy system of data pipelines. This made things simple.

ETL 40

ETL Data Lakes Database Data Pipeline 40

phData

JANUARY 31, 2024

Where Streamlit shines is creating interactive applications, not typical business intelligence dashboards and reporting. Snowflake Dynamic Tables are a new(ish) table type that enables building and managing data pipelines with simple SQL statements. that were previously all needed to put your app into production.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52