phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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Data Lakes Data Modeling Data Models Data Warehouse 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.

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Data Lakes Clustering Big Data Big Data 52

AWS Machine Learning Blog

DECEMBER 3, 2024

Although generative AI is fueling transformative innovations, enterprises may still experience sharply divided data silos when it comes to enterprise knowledge, in particular between unstructured content (such as PDFs, Word documents, and HTML pages), and structured data (real-time data and reports stored in databases or data lakes).

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AWS Database Data Silos Data Lakes 99

Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

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Apache Kafka ETL Data Lakes AWS 130

AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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AWS Data Governance Data Silos SQL 81

Dataconomy

SEPTEMBER 4, 2023

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. It uses natural language processing (NLP) techniques to extract valuable insights from textual data.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations GitHub | Website Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing, documentation, and profiling. With Great Expectations , data teams can express what they “expect” from their data using simple assertions.

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Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Pickl AI

FEBRUARY 23, 2025

Look for features such as scalability (the ability to handle growing datasets), performance (speed of processing), ease of use (user-friendly interfaces), integration capabilities (compatibility with existing systems), security measures (data protection features), and pricing models (licensing costs).

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 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. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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AI AI Data Warehouse ML 88

Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Flipboard

MAY 9, 2025

This pattern creates a comprehensive solution that transforms raw social media data into actionable business intelligence (BI) through advanced AI capabilities. 3B Instruct Amazon Bedrock, the system provides tailored marketing content that adds business value. By integrating LLMs such as Anthropics Claude 3.5

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IBM Journey to AI blog

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

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AI AI Data Scientist Data Governance 93

Alation

JULY 19, 2022

For instance, Alation can return wiki-like articles, conversations, and business intelligence objects, in addition to traditional tables. Documenting data science projects this way in a data catalog saves time and prevents data scientists from wasting resources on duplicate projects.

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Data Scientist Data Quality Data Science Data Analyst 59

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

Pickl AI

APRIL 9, 2025

Loading : Storing the transformed data in a target system like a data warehouse, data lake, or even a database. This stage involves optimizing the data for querying and analysis. Data Loading : Mechanisms for storing processed data in warehouses or lakes, ensuring optimal performance for querying and analysis.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. Enterprises might also have petabytes, if not exabytes, of valuable proprietary data stored in their mainframe that needs to be unlocked for new insights and ML/AI models.

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AI AI Data Quality Database 45

DagsHub

NOVEMBER 11, 2024

So, we must understand the different unstructured data types and effectively process them to uncover hidden patterns. Textual Data Textual data is one of the most common forms of unstructured data and can be in the format of documents, social media posts, emails, web pages, customer reviews, or conversation logs.

AI 52

AI AI Data Lakes Database 52

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.

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Data Warehouse Business Intelligence Business Intelligence Database 52

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.

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AWS Natural Language Processing Machine Learning Machine Learning 126

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.

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AI AI Machine Learning Machine Learning 74

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Business intelligence reports. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

Alation

APRIL 14, 2022

For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Pushing data to a data lake and assuming it is ready for use is shortsighted. On-premises business intelligence and databases.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

JUNE 9, 2021

We have an explosion, not only in the raw amount of data, but in the types of database systems for storing it ( db-engines.com ranks over 340) and architectures for managing it (from operational datastores to data lakes to cloud data warehouses). Organizations are drowning in a deluge of data.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

Alation

MARCH 21, 2023

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So

Analytics 52

Analytics Analytics Data Silos 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.

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Machine Learning Machine Learning Data Scientist ML 69

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. Amazon SageMaker Unified Studio , Amazon EMR 7.5.0 and higher, and AWS Glue 5.0

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AWS SQL Data Lakes Data Warehouse 132

AWS Machine Learning Blog

OCTOBER 11, 2024

Create business intelligence (BI) dashboards for visual representation and analysis of event data. A streamlined process should include steps to ensure that events are promptly detected, prioritized, acted upon, and documented for future reference and compliance purposes, enabling efficient operational event management at scale.

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AWS AI AI Data Lakes 97