IBM Journey to AI blog

APRIL 24, 2023

Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions. By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement.

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Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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Data Lakes Data Warehouse Data Quality Data Governance 59

Dataversity

MARCH 10, 2023

Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. However, AI adoption isn’t simple.

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Flipboard

DECEMBER 18, 2023

Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads. Learn more about the AWS zero-ETL future with newly launched AWS databases integrations with Amazon Redshift.

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Flipboard

MAY 24, 2023

Cloud-based data warehouse company Snowflake on Wednesday said that it was acquiring Neeva, a startup based in Mountain View, California, for an …

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Data Warehouse AI AI Computer Science 127

Dataconomy

APRIL 27, 2023

The capacity of computers to think, learn, make decisions, and be creative are all examples of what we mean when we talk about artificial intelligence (AI). Rapid progress in AI has been made in recent years due to an abundance of data, high-powered processing hardware, and complex algorithms. You can still get on the AI train!

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IBM Journey to AI blog

JUNE 15, 2023

The proliferation of data silos also inhibits the unification and enrichment of data which is essential to unlocking the new insights. Moreover, increased regulatory requirements make it harder for enterprises to democratize data access and scale the adoption of analytics and artificial intelligence (AI).

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Flipboard

JUNE 17, 2023

Our guest on the GeekWire Podcast is business and tech leader Bob Muglia, a startup investor and advisor who played a pivotal role in Microsoft’s database and server products, and was CEO of data warehouse company Snowflake Computing.

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AWS Machine Learning Blog

APRIL 3, 2025

OMRONs data strategyrepresented on ODAPalso allowed the organization to unlock generative AI use cases focused on tangible business outcomes and enhanced productivity. When needed, the system can access an ODAP data warehouse to retrieve additional information.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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Data Lakes Data Warehouse Database Big Data 98

Dataconomy

OCTOBER 8, 2024

Generative artificial intelligence is the talk of the town in the technology world today. Space and Time’s creator SxT Labs has created three technologies that underpin its verifiable compute layer, including a blockchain indexer, a distributed data warehouse and a zero-knowledge coprocessor.

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Flipboard

JANUARY 6, 2025

The agency wanted to use AI [artificial intelligence] and ML to automate document digitization, and it also needed help understanding each document it digitizes, says Duan. The federal government agency Precise worked with needed to automate manual processes for document intake and image processing.

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IBM Journey to AI blog

APRIL 25, 2023

To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy. To effectively use raw data, it often needs to be curated within a data warehouse.

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Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

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IBM Journey to AI blog

OCTOBER 4, 2023

Artificial intelligence (AI) is now at the forefront of how enterprises work with data to help reinvent operations, improve customer experiences, and maintain a competitive advantage. It’s no longer a nice-to-have, but an integral part of a successful data strategy.

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Towards AI

FEBRUARY 21, 2023

The arrival of Artificial Intelligence in the business world has been a true game changer. Introduction Here we look at the signs that your business is ready for AI solutions, including data collection and storage requirements, staff training needs, and cost implications.

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Dataconomy

FEBRUARY 12, 2024

ELT advocates for loading raw data directly into storage systems, often cloud-based, before transforming it as necessary. This shift leverages the capabilities of modern data warehouses, enabling faster data ingestion and reducing the complexities associated with traditional transformation-heavy ETL processes.

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Dataconomy

JUNE 1, 2023

By automating the integration of all Fabric workloads into OneLake, Microsoft eliminates the need for developers, analysts, and business users to create their own data silos. This approach not only improves performance by eliminating the need for separate data warehouses but also results in substantial cost savings for customers.

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Smart Data Collective

DECEMBER 13, 2022

Five Best Practices for Data Analytics. Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Select a Storage Platform.

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IBM Journey to AI blog

MAY 9, 2023

Today is a revolutionary moment for Artificial Intelligence (AI). With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments.

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AI AI Data Warehouse Machine Learning 110

phData

NOVEMBER 4, 2024

Its cloud-native architecture, combined with robust data-sharing capabilities, allows businesses to easily leverage cutting-edge tools from partners like Dataiku, fostering innovation and driving more insightful, data-driven outcomes. Dataiku and Snowflake: A Good Combo?

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Machine Learning Machine Learning Data Science ML 69

Towards AI

FEBRUARY 13, 2023

According to Yann LeCun, Chief Artificial Intelligence Scientist at Meta, the reason it was boring was that it was made safe. Three months before ChatGPT’s launch in November, Meta, Facebook’s parent company, introduced a similar chatbot, Blenderbot. However, Blenderbot failed to create the same excitement as ChatGPT.

AI 98

AI AI Data Warehouse Data Lakes 98

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

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Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

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Tableau Data Warehouse Database Data Analyst 98

Smart Data Collective

NOVEMBER 18, 2020

Data Science is an activity that focuses on data analysis and finding the best solutions based on it. Then artificial intelligence advances became more widely used, which made it possible to include optimization and informatics in analysis methods. Data Mining is an important research process. Practical experience.

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Precisely

NOVEMBER 11, 2024

This involves integrating customer data across various channels – like your CRM systems, data warehouses, and more – so that the most relevant and up-to-date information is used consistently in your customer interactions. Focus on high-quality data. Data quality is essential for personalization efforts.

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IBM Journey to AI blog

OCTOBER 22, 2024

Data is the differentiator as business leaders look to utilize their competitive edge as they implement generative AI (gen AI). Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement.

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ODSC - Open Data Science

MAY 7, 2024

In this episode, James Serra, author of “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh” joins us to discuss his book and dive into the current state and possible future of data architectures.

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Data Warehouse Data Lakes Data Science Big Data 52

Precisely

NOVEMBER 11, 2024

This involves integrating customer data across various channels – like your CRM systems, data warehouses, and more – so that the most relevant and up-to-date information is used consistently in your customer interactions. Focus on high-quality data. Data quality is essential for personalization efforts.

Data Silos 52

Data Silos Data Warehouse Artificial Intelligence Artificial Intelligence 52

IBM Journey to AI blog

MAY 9, 2023

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

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Dataversity

NOVEMBER 29, 2021

There’s been a lot of talk about the modern data stack recently. Much of this focus is placed on the innovations around the movement, transformation, and governance of data as it relates to the shift from on-premise to cloud data warehouse-centric architectures.

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Analytics Analytics Data Warehouse Cloud Data 52

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. The Stanford Institute for Human-Centered Artificial Intelligence’s Center for Research on Foundation Models (CRFM) recently outlined the many risks of foundation models, as well as opportunities. Trustworthiness is critical.

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AWS Machine Learning Blog

JANUARY 6, 2023

TR has a wealth of data that could be used for personalization that has been collected from customer interactions and stored within a centralized data warehouse. The user interactions data from various sources is persisted in their data warehouse. The following diagram illustrates the ML training pipeline.

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ODSC - Open Data Science

JUNE 22, 2023

These are called data lakes. What Are Data Lakes? Unlike databases and data warehouses, data lakes can store data in raw and unstructured forms. This feature is important because it allows data lakes to hold a larger amount of data and store it faster.

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Data Lakes Data Warehouse Database Data Science 98

ODSC - Open Data Science

AUGUST 23, 2023

Run pandas at scale on your data warehouse Most enterprise data teams store their data in a database or data warehouse, such as Snowflake, BigQuery, or DuckDB. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

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Data Warehouse Data Science Database SQL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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