Cloudera

JUNE 7, 2022

Big data is cool again. As the company who taught the world the value of big data, we always knew it would be. But this is not your grandfather’s big data. It has evolved into something new – hybrid data. Choose a hybrid data-first strategy to deliver value faster.

IT 113

IT Data Architecture Unstructured Data Big Data 113

IBM Big Data Hub

MAY 9, 2023

It drives an AI governance solution without the excessive costs of switching from your current data science platform. The resulting automation drives scalability and accountability by capturing model development time and metadata, offering post-deployment model monitoring, and allowing for customized workflows.

Metadata 71

Metadata Risk Experimentation Dashboards 71

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

Alation

FEBRUARY 20, 2020

We define it as this: Data acquisition is the processes for bringing data that has been created by a source outside the organization, into the organization, for production use. Prior to the Big Data revolution, companies were inward-looking in terms of data. THE NEED FOR METADATA TOOLS.

Metadata 52

Metadata IT Data Governance Data Warehouse 52

AWS Big Data

MAY 4, 2023

Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. Over the last decade, we’ve seen a surge in data science frameworks coming to fruition, along with mass adoption by the data science community. Data scientists have access to the Jupyter notebook hosted on SageMaker.

Data Processing 94

Data Processing Metadata Informatics Interactive 94

Cloudera

FEBRUARY 11, 2019

The January 2019 “Magic Quadrant for Data Management Solutions for Analytics” provides valuable insights into the status, direction, and players in the DMSA market. In this blog, we share our takeaways as they relate to the DMSA market trends. 3. Expansion beyond core data management. Cloudera’s Positioning.

Management 43

Management Metadata Analytics Machine Learning 43

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance toolkit lets you direct, manage and monitor AI activities without the expense of switching your data science platform, even for models developed using third-party tools.

Risk 71

Risk Modeling Management Metadata 71

IBM Big Data Hub

SEPTEMBER 20, 2022

Automated, integrated data science tools help build, deploy, and monitor AI models. Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards. It’s not just about granting proper access to data science teams.

Metadata 56

Metadata Machine Learning Data-driven Modeling 56

Cloudera

JUNE 18, 2022

The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. It provided the concept of a database, schemas, and tables for describing the structure of a data lake in a way that let BI tools traverse the data efficiently.

Data Lake 94

Data Lake Data Warehouse Machine Learning Cost-Benefit 94

Cloudera

SEPTEMBER 28, 2020

Cloudera delivers an enterprise data cloud that enables companies to build end-to-end data pipelines for hybrid cloud, spanning edge devices to public or private cloud, with integrated security and governance underpinning it to protect customers data. Data Science and machine learning workloads using CDSW.

Testing 132

Testing Metadata Risk Data Science 132

AWS Big Data

JUNE 7, 2023

This is a guest blog post co-written with Sumesh M R from Cargotec and Tero Karttunen from Knowit Finland. They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 83

Data Warehouse Cost-Benefit Metadata Data-driven 83

IBM Big Data Hub

MAY 9, 2022

As discussed in the previous section data virtualization and data cataloging help get the right data to the right people by making it easier to find the data that best fits their needs and access it. Automated metadata generation is essential in order to turn a manual process into one that is better controlled.

Management 56

Management Data Quality Metadata Data Integration 56

Data Virtualization

OCTOBER 18, 2022

The post The Data Warehouse is Dead, Long Live the Data Warehouse, Part I appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information. In times of potentially troublesome change, the apparent paradox and inner poetry of these.

Data Warehouse 98

Data Warehouse ROI Data Integration Internet of Things 98

AWS Big Data

JUNE 6, 2023

This is a guest blog post by Mira Daniels and Sean Whitfield from SumUp. The Data Science teams also use this data for churn prediction and CLTV modeling. Given that the only source to access all raw data is by exporting it to BigQuery (first), data accessibility becomes challenging if BigQuery isn’t your DWH solution.

Analytics 55

Analytics Data Lake Testing Optimization 55

Cloudera

NOVEMBER 6, 2017

Popular products like Cloudera Data Science Workbench , Analytic DB , Operational DB , and Essentials can be run natively in cloud environments, deploying the cloud service’s resources much the same way they would on-premises. Security policies are set once and implemented uniformly upon data and users anywhere.

Metadata 40

Metadata Data Science Enterprise Risk 40

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Research VP, Business Analytics and Data Science.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Alation

FEBRUARY 13, 2020

His focus is on business intelligence, analytics, big data, and data warehousing. So we came up with a concept called a Unified Data Set (UDS). Graves: One of the tools that we’re utilizing is Alation, which is a data catalog tool. Subscribe to Alation's Blog. Sharon Graves: Certainly. Thank you Sharon.

Digital Transformation 52

Digital Transformation Data-driven Business Intelligence Big Data 52

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

IBM Big Data Hub

OCTOBER 4, 2023

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. How does an open data lakehouse architecture support AI? All of this supports the use of AI.

Data Lake 78

Data Lake Metadata Cost-Benefit Data Warehouse 78

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 138

Machine Learning Data Science Data Lake Modeling 138

IBM Big Data Hub

FEBRUARY 22, 2024

IBM’s Scaled Data Science Method , an extension of CRISP-DM, offers governance across the AI model lifecycle informed by collaborative input from data scientists, industrial-organizational psychologists, designers, communication specialists and others. This should all be baked into the interpretable, findable metadata).

Metadata 93

Metadata Measurement Risk Modeling 93

Cloudera

JANUARY 22, 2019

In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights. Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. What is Big Data Fabric? Data access.

Big Data 45

Big Data Data Lake Internet of Things Enterprise 45

IBM Big Data Hub

JULY 13, 2023

Data science tasks such as machine learning also greatly benefit from good data integrity. When an underlying machine learning model is being trained on data records that are trustworthy and accurate, the better that model will be at making business predictions or automating tasks. appeared first on IBM Blog.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. 2 “Exposing The Data Mesh Blind Side ” Forrester.

Management 80

Management Metadata Data Architecture Data Lake 80

IBM Big Data Hub

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture. Data governance. The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. Data science and MLOps.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

Alation

JANUARY 17, 2023

Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? What Are the Benefits of a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Smart Data Collective

AUGUST 5, 2021

And, finally, if the amount of data exceeds on certain occasions, the storage may run out of memory. This means that the process would die in the middle of the final writes, making consumers distinctly read the input data frames. Delta Lake uses versioned Parquet files to store data in the cloud. from Spark 2.4. .

Data Processing 143

Data Processing Metadata Broadcasting Statistics 143

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. It’s More Important to Know What Your Data Means Than Where It Is.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support. What capabilities are included in watsonx.ai?

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

Cloudera

AUGUST 21, 2020

And next to those legacy ERP, HCM, SCM and CRM systems, that mysterious elephant in the room – that “Big Data” platform running in the data center that is driving much of the company’s analytics and BI – looks like a great potential candidate. . Streaming data analytics. . Data science & engineering.

Cost-Benefit 76

Cost-Benefit Big Data ROI Risk 76

Cloudera

MARCH 6, 2018

If catalog metadata and business definitions live with transient compute resources, they will be lost, requiring work to recreate later and making auditing impossible. Nimbly run many distinct applications against shared data. Altus SDX includes a shared metadata catalog that puts data in context.

Analytics 40

Analytics Metadata Recreation/Entertainment Big Data 40

Cloudera

SEPTEMBER 6, 2018

Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews. Spark also enables data science at scale. Cloudera Enterprise.

Machine Learning 40

Machine Learning Big Data Risk Unstructured Data 40

IBM Big Data Hub

MAY 9, 2023

Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1] 1] Users can access data through a single point of entry, with a shared metadata layer across clouds and on-premises environments.

Data Warehouse 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107

IBM Big Data Hub

FEBRUARY 8, 2023

This comprehensive solution comes without the excessive costs of switching from your current data science platform. Automate the capture of model metadata and increase predictive accuracy to identify how AI is used and where models need to be reworked. Want to see how IBM AI governance can help your organization?

Risk Management 52

Risk Management Risk Modeling Metadata 52

Cloudera

JUNE 25, 2019

Hybrid and multi-cloud – provides choice to manage, analyze and experiment with data in any public cloud and in private data centers for maximum choice and flexibility. Shared Data Experience (SDX) – Enabling consistent security, governance, and control across data stores and cloud services.

Enterprise 88

Enterprise Machine Learning Recreation/Entertainment IoT 88