AWS Big Data

MARCH 27, 2024

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. This means you no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog.

Data Analytics 93

Data Analytics Analytics Data Warehouse Data Lake 93

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. Azure Blob Storage serves as the data lake to store raw data.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 97

Data Governance Unstructured Data Metadata Data Lake 97

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

AWS Big Data

MARCH 29, 2024

We also lacked a data buffer, risking potential data loss during outages. Such constraints impeded innovation and increased risks. Amazon MSK enables us to tailor the data retention duration to our specific requirements, ranging from seconds to unlimited duration.

Cost-Benefit 93

Cost-Benefit Data-driven Metrics Management 93

BizAcuity

APRIL 1, 2023

This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organization.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Lake Internet of Things 52

AWS Big Data

NOVEMBER 30, 2023

In this post, we explore how to use the AWS Glue native connector for Teradata Vantage to streamline data integrations and unlock the full potential of your data. Businesses often rely on Amazon Simple Storage Service (Amazon S3) for storing large amounts of data from various data sources in a cost-effective and secure manner.

IT 98

IT Visualization Machine Learning Data Integration 98

erwin

OCTOBER 8, 2020

No less daunting, your next step is to re-point or even re-platform your data movement processes. And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. The metadata-driven suite automatically finds, models, ingests, catalogs and governs cloud data assets.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

CIO Business Intelligence

FEBRUARY 22, 2024

The perfect ESG software would encompass all lifecycle elements of an ESG strategy, be a potent program management tool, a risk management tool, provider of analytics, and a vehicle for accountability and verification.” That’s where the single source of truth comes into perspective and increases performance,” Karcher says.

Software 86

Software Reporting KPI Enterprise 86

Cloudera

SEPTEMBER 12, 2022

A data fabric answers perhaps the biggest question of all: what data do we have to work with? Managing and making individual data sources available through traditional enterprise data integration, and when end users request them, simply does not scale — especially in light of a growing number of sources and volume.

Unstructured Data 74

Unstructured Data Data Architecture Data Lake Snapshot 74

AWS Big Data

JUNE 28, 2023

Furthermore, the time required to build or change pipelines makes the data unfit for near-real-time use cases such as detecting fraudulent transactions, placing online ads, and tracking passenger train schedules. Improving the zero-ETL performance is a continuous goal for AWS.

Analytics 71

Analytics Data Warehouse Data Lake Data-driven 71

erwin

JANUARY 11, 2019

The sad truth is that high-paid knowledge workers like data scientists spend up to 80 percent of their time finding and understanding source data and resolving errors or inconsistencies, rather than analyzing it for real value. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

Grooper

MARCH 5, 2020

However, if you haven’t explicitly defined what information stewardship is, or there is some confusion regarding roles and responsibilities for your precious data – your data-related projects are at a high risk for failure. Lower cost data processes. More effective business process execution.

Data Lake 98

Data Lake Metadata Data Quality Software 98

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Enrichment typically involves adding demographic, behavioral, and geolocation data.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

CIO Business Intelligence

OCTOBER 1, 2022

It requires taking data from equipment sensors, applying advanced analytics to derive descriptive and predictive insights, and automating corrective actions. The end-to-end process requires several steps, including data integration and algorithm development, training, and deployment.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. Although data lakes resemble data vaults, a data vault provides more features of a data warehouse.

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

erwin

JULY 17, 2019

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement. And do it without the risk of breaking everything.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

BI-Survey

NOVEMBER 23, 2021

Data-based insights are being used to automate decisions. The goal is to make business processes faster, more efficient and less vulnerable to risk. Thus, alternative data architecture concepts have emerged, such as the data lake and the data lakehouse. Instead, distributed data must be linkable as needed.

Digital Transformation 52

Digital Transformation Data Warehouse Data Lake Data-driven 52

Cloudera

MAY 16, 2018

This approach also relates to monitoring internal fiduciary risk by tying separate events together, such as a large position (relative to historic norms) being taken immediately after the risk model that would have flagged it was modified in a separate system. However, in this case, that output is ingested into a data lake.

Data Lake 42

Data Lake Risk Visualization Unstructured Data 42

Cloudera

JUNE 1, 2021

In summary, the next chapter for Cloudera will allow us to concentrate our efforts on strategic business opportunities and take thoughtful risks that help accelerate growth. Said simply, Datacoral offers a fully-managed service for worry-free data integrations.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

erwin

AUGUST 31, 2023

Reduced Data Redundancy : By eliminating data duplication, it optimizes storage and enhances data quality, reducing errors and discrepancies. Efficient Development : Accurate data models expedite database development, leading to efficient data integration, migration, and application development.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

IBM Big Data Hub

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.

Cost-Benefit 79

Cost-Benefit Metadata Data Governance Modeling 79

AWS Big Data

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 78

Data Quality Data Lake Data-driven Metrics 78

Cloudera

JULY 18, 2018

For those asking big questions, in the case of healthcare, an incredible amount of insight remains hidden away in troves of clinical notes, EHR data, medical images, and omics data. To arrive at quality data, organizations are spending significant levels of effort on data integration, visualization, and deployment activities.

Machine Learning 51

Machine Learning Predictive Analytics Analytics Prescriptive Analytics 51

Alation

JANUARY 27, 2022

Data analytics is a way to make sense of raw data. Raw data includes market research, sales data, customer transactions, and more. Analytics can identify patterns that depict risks, opportunities, and trends. And historical data can be used to inform predictive analytic models, which forecast the future.

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52

Ontotext

MARCH 8, 2023

Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions. A risk issue in one financial institution could result in a domino effect for many. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

DataKitchen

DECEMBER 9, 2022

Observability in DataOps refers to the ability to monitor and understand the performance and behavior of data-related systems and processes, and to use that information to improve the quality and speed of data-driven decision making.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 86

Metadata Data Lake Optimization Strategy 86

AWS Big Data

SEPTEMBER 22, 2023

At Stitch Fix, we have been powered by data science since its foundation and rely on many modern data lake and data processing technologies. In our infrastructure, Apache Kafka has emerged as a powerful tool for managing event streams and facilitating real-time data processing.

Management 105

Management Metrics Cost-Benefit Data Lake 105

Jet Global

OCTOBER 7, 2020

In the future, customers will be able to deploy Data Entities and replicate transactional tables in an Azure Data Lake. Reporting and analytics solutions from insightsoftware eliminate complexity, reduce cost, and decrease the risk of lengthy implementations. Microsoft is currently developing this capability.

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

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.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

AWS Big Data

JUNE 12, 2023

By harnessing the power of streaming data, organizations are able to stay ahead of real-time events and make quick, informed decisions. With the ability to monitor and respond to real-time events, organizations are better equipped to capitalize on opportunities and mitigate risks as they arise.

Management 82

Management Metadata Testing Internet of Things 82

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. Probably the best one-liner I’ve encountered is the analogy that: DG is to data assets as HR is to people. in lieu of simply landing in a data lake. a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84