AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 115

Data Quality Measurement Testing Visualization 115

DataKitchen

NOVEMBER 29, 2021

The rise of distributed data architectures like Data Mesh will combine with DataOps automation to give rise to Hub-Spoke architectures that deftly blend the benefits of centralization and decentralization. For example, a Hub-Spoke architecture could integrate data from a multitude of sources into a data lake.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

CIO Business Intelligence

DECEMBER 20, 2024

Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs. Data must be able to freely move to and from data warehouses, data lakes, and data marts, and interfaces must make it easy for users to consume that data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

CIO Business Intelligence

APRIL 8, 2025

From reactive fixes to embedded data quality Vipin Jain Breaking free from recurring data issues requires more than cleanup sprints it demands an enterprise-wide shift toward proactive, intentional design. Data quality must be embedded into how data is structured, governed, measured and operationalized.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

DataKitchen

SEPTEMBER 18, 2024

These leaders are expected to influence organizational behavior without direct authority, leading to what DataKitchen CEO Christopher Bergh described as “data nags”—individuals who know what’s wrong but struggle to get others to act. Who should make the change (data engineers, system owners, or data quality professionals).

Scorecard 177

Scorecard Data Quality Measurement Testing 177

AWS Big Data

JANUARY 15, 2025

cycle_end"', "sagemakedatalakeenvironment_sub_db", ctas_approach=False) A similar approach is used to connect to shared data from Amazon Redshift, which is also shared using Amazon DataZone. datazone_env_twinsimsilverdata"."cycle_end";') She can reached via LinkedIn. Siamak Nariman is a Senior Product Manager at AWS.

IoT 111

IoT Machine Learning Metadata Data-driven 111

Corinium

JUNE 6, 2019

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. What is the most common mistake people make around data? Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

OCTOBER 9, 2024

The first is to experiment with tactical deployments to learn more about the technology and data use. This is known as data preparation, a short-term measure that identifies data sets and defines data requirements. That’s why many enterprises are adopting a two-pronged approach to GenAI.

Strategy 143

Strategy Modeling Management Data Lake 143

AWS Big Data

JULY 29, 2024

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.

Metadata 126

Metadata Snapshot Data Lake Metrics 126

AWS Big Data

OCTOBER 21, 2024

However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture. To incorporate this third-party data, AWS Data Exchange is the logical choice.

Sales 115

Sales Data-driven Data Processing Key Performance Indicator 115

DataKitchen

JANUARY 3, 2022

There’s a recent trend toward people creating data lake or data warehouse patterns and calling it data enablement or a data hub. DataOps expands upon this approach by focusing on the processes and workflows that create data enablement and business analytics. DataOps Process Hub. Stop Firefighting.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

AWS Big Data

DECEMBER 5, 2023

He has worked on building and tuning data warehouse and data lake solutions for over 15 years. He is passionate about helping customers modernize their data platforms with efficient, performant, and scalable analytic solutions. Outside of work she enjoys traveling and trying new cuisines.

Measurement 105

Measurement Dashboards Data Warehouse Analytics 105

CIO Business Intelligence

OCTOBER 25, 2022

Data & Analytics is delivering on its promise. Every day, it helps countless organizations do everything from measure their ESG impact to create new streams of revenue, and consequently, companies without strong data cultures or concrete plans to build one are feeling the pressure. So, they built a data-lake.

Data Strategy 145

Data Strategy Strategy Data Lake Data-driven 145

AWS Big Data

OCTOBER 9, 2024

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. Compare ongoing data that is replicated from the source on-premises database to the target S3 data lake.

Data Quality 113

Data Quality Data Lake Data Warehouse Metrics 113

O'Reilly on Data

OCTOBER 19, 2021

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. The iteration cycles should be measured in hours or days, not in months.

IT 364

IT Testing Experimentation Software 364

AWS Big Data

JUNE 21, 2023

Nonetheless, many of the same customers using DynamoDB would also like to be able to perform aggregations and ad hoc queries against their data to measure important KPIs that are pertinent to their business. A typical ask for this data may be to identify sales trends as well as sales growth on a yearly, monthly, or even daily basis.

Data Warehouse 105

Data Warehouse Data Lake OLAP Cost-Benefit 105

AWS Big Data

FEBRUARY 22, 2023

In this post, we show how Ruparupa implemented an incrementally updated data lake to get insights into their business using Amazon Simple Storage Service (Amazon S3), AWS Glue , Apache Hudi , and Amazon QuickSight. An AWS Glue ETL job, using the Apache Hudi connector, updates the S3 data lake hourly with incremental data.

Data Lake 52

Data Lake Dashboards Cost-Benefit Data Warehouse 52

Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. Well, let’s find out. Artificial intelligence (AI). You can therefore trust its reliability.

Cost-Benefit 138

Cost-Benefit Data Lake Software Machine Learning 138

AWS Big Data

OCTOBER 3, 2023

In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) data lake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.

Optimization 126

Optimization Snapshot Data Lake Metadata 126

CIO Business Intelligence

AUGUST 9, 2024

The alternative to synthetic data is to manually anonymize and de-identify data sets, but this requires more time and effort and has a higher error rate. The European AI Act also talks about synthetic data, citing them as a possible measure to mitigate the risks associated with the use of personal data for training AI systems.

Risk 138

Risk Measurement Data Collection Data Lake 138

CIO Business Intelligence

MARCH 2, 2023

The company measures the success of these efforts by business outcomes, not the success of the automation itself, he adds. This engine will be deeply integrated into our data lake to enable truly individualized student support at the right time, through the best channel,” he adds.

Data Lake 143

Data Lake Forecasting B2B Optimization 143

AWS Big Data

AUGUST 2, 2023

In this blog, we will walk through how we can apply existing enterprise data to better understand and estimate Scope 1 carbon footprint using Amazon Simple Storage Service (S3) and Amazon Athena , a serverless interactive analytics service that makes it easy to analyze data using standard SQL.

Data Lake 96

Data Lake Measurement Visualization Data Architecture 96

AWS Big Data

MARCH 7, 2023

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. A data hub contains data at multiple levels of granularity and is often not integrated.

Analytics 96

Analytics Data Warehouse Data Lake Metadata 96

AWS Big Data

NOVEMBER 8, 2023

Amazon Redshift Serverless is a fully managed cloud data warehouse that allows you to seamlessly create your data warehouse with no infrastructure management required. Redshift Serverless measures data warehouse capacity in Redshift Processing Units (RPUs), which are part of the compute resources.

Data Lake 103

Data Lake Data Warehouse Cost-Benefit Optimization 103

AWS Big Data

NOVEMBER 7, 2024

Importantly, the robust security measures of Amazon Redshift remain fully enforced, and the quality of the generated SQL continues to improve over time by enabling query history sharing across users. Sushmita is based out of Tampa, FL and enjoys traveling, reading and playing tennis.

Metadata 105

Metadata Sales Data Warehouse Optimization 105

CIO Business Intelligence

MAY 5, 2023

While enterprise IT orgs by and large are taking a measured approach , some early movers are showing impressive results. As a Microsoft Azure shop, CarMax relies on Azure Data Lake, an essential component of the company’s AI output, the CIO notes.

Digital Transformation 145

Digital Transformation Cost-Benefit Business Driver Machine Learning 145

DataKitchen

NOVEMBER 18, 2022

concrete expectations for run schedules, run durations, data quality, and upstream and downstream dependencies. Observability users are then able to see and measure the variance between expectations and reality during and after each run. And she’ll know when newer data will arrive. Storing Run Data for Analysis.

Testing 130

Testing Statistics Measurement Dashboards 130

Cloudera

APRIL 20, 2022

For example, people at high risk for hospitalization upon infection, each received an oxy pulse meter and were asked to either call into a hotline if their measurements were outside of a range, or upload each measurement to a portal.

Insurance 101

Insurance Measurement Data Lake Risk 101

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. Additionally, data is extracted from vendor APIs that includes data related to product, marketing, and customer experience.

Data Warehouse 111

Data Warehouse Analytics Data Lake Data Science 111

CIO Business Intelligence

JANUARY 10, 2024

Measure often, monitor constantly Proper insights require both a knowledge of desired business outcomes overall and for each business unit, and ongoing monitoring of key metrics. But for other tools where latency isn’t critical, we don’t measure it.” “What exactly happens if you go over and what will they charge you?”

Cost-Benefit 124

Cost-Benefit Measurement Optimization Metrics 124

Cloudera

AUGUST 18, 2021

Data processed at the edge or in the cloud, for instance, is not effective if it follows the traditional lifecycle of “ingest, process, land, and analyze.” If the data goes into a data lake before analysis, extracting it can get pretty complex and time-consuming. Improving Patient Care.

Data Lake 88

Data Lake Internet of Things IoT Data-driven 88

DataKitchen

OCTOBER 11, 2021

Finally, when your implementation is complete, you can track and measure your process. Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. The automated orchestration published the data to an AWS S3 Data Lake.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

FEBRUARY 29, 2024

To provide a response that includes the enterprise context, each user prompt needs to be augmented with a combination of insights from structured data from the data warehouse and unstructured data from the enterprise data lake.

Data Governance 123

Data Governance Unstructured Data Metadata Data Lake 123

BI-Survey

AUGUST 21, 2020

Advanced analytics and new ways of working with data also create new requirements that surpass the traditional concepts. But what are the right measures to make the data warehouse and BI fit for the future? Can the basic nature of the data be proactively improved? What role do technology and IT infrastructure play?

Data Warehouse 98

Data Warehouse Data Lake Data Governance Data Architecture 98

CIO Business Intelligence

MARCH 22, 2022

Which type(s) of storage consolidation you use depends on the data you generate and collect. . One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Focus on a specific business problem to be solved.

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

CIO Business Intelligence

NOVEMBER 28, 2023

The knock-on impact of this lack of analyst coverage is a paucity of data about monies being spent on data management. In reality MDM ( master data management ) means Major Data Mess at most large firms, the end result of 20-plus years of throwing data into data warehouses and data lakes without a comprehensive data strategy.

Management 131

Management Data Architecture Data Lake Data Strategy 131

Jen Stirrup

MAY 5, 2020

Azure allows you to protect your enterprise data assets, using Azure Active Directory and setting up your virtual network. Other technologies, such as Azure Data Factory, can help process large amounts of data around in the cloud. So, Azure Databricks connects to many different data sources. Azure Data Lake Store.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89