CIO Business Intelligence

OCTOBER 13, 2023

Data architecture is a complex and varied field and different organizations and industries have unique needs when it comes to their data architects. Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

AWS Big Data

JULY 21, 2023

This leads to having data across many instances of data warehouses and data lakes using a modern data architecture in separate AWS accounts. We recently announced the integration of Amazon Redshift data sharing with AWS Lake Formation.

Data Lake 84

Data Lake Data Warehouse Marketing Management 84

Ontotext

APRIL 10, 2024

In most enterprises data teams lack a data map and data asset inventory and are often unaware of data that exists across the organization, its associated profile, quality and associated metadata. Teams can’t access data to build their business use cases. For example, a product data tag is basic metadata.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

AWS Big Data

JANUARY 31, 2023

We have collected some of the key talks and solutions on data governance, data mesh, and modern data architecture published and presented in AWS re:Invent 2022, and a few data lake solutions built by customers and AWS Partners for easy reference.

Data Lake 79

Data Lake Data Governance Data Architecture Data Warehouse 79

AWS Big Data

NOVEMBER 29, 2023

For more information about performance improvement capabilities, refer to the list of announcements below. Zero-ETL integration also enables you to load and analyze data from multiple operational database clusters in a new or existing Amazon Redshift instance to derive holistic insights across many applications.

Data Warehouse 100

Data Warehouse Data Lake Analytics Machine Learning 100

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 83

Enterprise Data Warehouse Data Lake Optimization 83

AWS Big Data

MARCH 25, 2024

To learn more about RAG, refer to Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart. A RAG-based generative AI application can only produce generic responses based on its training data and the relevant documents in the knowledge base.

Data Lake 90

Data Lake Unstructured Data Management Modeling 90

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. Monitoring Job Metadata.

Testing 157

Testing Metadata Dashboards Statistics 157

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 3: Open Performance.

Metadata 90

Metadata Data Architecture Machine Learning Cost-Benefit 90

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

Cloudera

SEPTEMBER 15, 2022

Mark: The first element in the process is the link between the source data and the entry point into the data platform. At Ramsey International (RI), we refer to that layer in the architecture as the foundation, but others call it a staging area, raw zone, or even a source data lake. What is a data fabric?

Data Lake 81

Data Lake Data Architecture Data-driven Data Warehouse 81

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 123

Metadata Cost-Benefit Enterprise Interactive 123

AWS Big Data

JANUARY 22, 2024

Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling. In this approach, teams responsible for generating data are referred to as producers.

Data-driven 85

Data-driven Advertising Metadata Data Architecture 85

AWS Big Data

JANUARY 8, 2024

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. To access your data from Timestream, you need to install the Timestream plugin for Grafana.

Analytics 111

Analytics IoT Data-driven Snapshot 111

Peter James Thomas

AUGUST 18, 2018

Convergent Evolution refers to something else. That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Of course some architectures featured both paradigms as well. This required additional investments in metadata. So far so simple. This is the essence of Convergent Evolution.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. with Apache Spark version 3.3.0)

Data Lake 80

Data Lake Dashboards Metrics Metadata 80

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses. RDF vs LPG?

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

AWS Big Data

APRIL 28, 2023

This will include how to configure Okta, AWS Lake Formation , and a business intelligence tool to enable SAML-based federated use of Athena for an enterprise BI activity. When building a scalable data architecture on AWS, giving autonomy and ownership to the data domains are crucial for the success of the platform.

Data Governance 67

Data Governance Management Data-driven Data Lake 67

AWS Big Data

OCTOBER 25, 2023

But most important of all, the assumed dormant value in the unstructured data is a question mark, which can only be answered after these sophisticated techniques have been applied. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly. The solution integrates data in three tiers.

Unstructured Data 118

Unstructured Data Metadata Management Analytics 118

AWS Big Data

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. What’s new with Amazon Redshift Want to learn more about the most recent features launched in Amazon Redshift?

Data Warehouse 98

Data Warehouse Machine Learning Data-driven Data Lake 98

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. Data democratization uses a fit-for-purpose data architecture that is designed for the way today’s businesses operate, in real-time.

Data Architecture 65

Data Architecture Data Lake Machine Learning Data Governance 65