DataKitchen

AUGUST 3, 2021

First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt. The post What is a Data Mesh?

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

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. Query> An AI, Chat GPT wrote this blog post, why should I read it? .

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

AWS Big Data

DECEMBER 4, 2024

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

Data Integration 97

Data Integration Data Lake Statistics Data-driven 97

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 93

Data Lake Analytics Snapshot Data Quality 93

DataKitchen

JANUARY 25, 2022

Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . For example, DataOps can be used to automate data integration.

Consulting 244

Consulting Testing Data Lake Data Quality 244

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

Data Quality 75

Data Quality Data Lake Visualization Data-driven 75

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 118

Metadata Snapshot Data Lake Metrics 118

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

AWS Big Data

DECEMBER 4, 2024

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. For more information, refer to What are deletion vectors?

Snapshot 84

Snapshot Metadata Data Lake Optimization 84

DataKitchen

NOVEMBER 10, 2023

The core issue plaguing many organizations is the presence of out-of-control databases or data lakes characterized by: Unrestrained Data Changes: Numerous users and tools incessantly alter data, leading to a tumultuous environment. Monitor freshness, schema changes, volume, and column health are standard.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

datapine

APRIL 15, 2020

You will need to continually return to your business dashboard to make sure that it’s working, the data is accurate and it’s still answering the right questions in the most effective way. Testing will eliminate lots of data quality challenges and bring a test-first approach through your agile cycle.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

AWS Big Data

JULY 25, 2024

This blog post is co-written with Raj Samineni from ATPCO. In today’s data-driven world, companies across industries recognize the immense value of data in making decisions, driving innovation, and building new products to serve their customers. Choose the Amazon DataZone blueprint you want to enable.

Data Lake 105

Data Lake Metadata Sales Publishing 105

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 113

Data Lake Data Warehouse Management Risk 113

IBM Big Data Hub

MAY 9, 2023

Data: the foundation of your foundation model Data quality matters. An AI model trained on biased or toxic data will naturally tend to produce biased or toxic outputs. When objectionable data is identified, we remove it, retrain the model, and repeat. Data curation is a task that’s never truly finished.

Enterprise 128

Enterprise Technology Modeling Cost-Benefit 128

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Alation

JUNE 17, 2021

Lastly, active data governance simplifies stewardship tasks of all kinds. Tehnical stewards have the tools to monitor data quality, access, and access control. A compliance steward is empowered to monitor sensitive data and usage sharing policies at scale. The Data Swamp Problem. Subscribe to Alation's Blog.

Data Governance 98

Data Governance Data Lake Insurance Enterprise 98

DataKitchen

OCTOBER 11, 2021

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. All the code, Talend job, and the BI report are version controlled using Git.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

JANUARY 30, 2023

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities. Choose Create role.

Insurance 86

Insurance Data Lake Data-driven Management 86

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Forrester ). Gartner ).

Data Architecture 79

Data Architecture Data Lake Data Warehouse Metadata 79

Cloudera

MARCH 28, 2024

Among the most common challenges to achieving AI adoption at scale were data quality and availability (36%), scalability and deployment (36%), integration with existing systems and processes (35%), and change management and organizational culture (34%).

Data Architecture 94

Data Architecture Strategy Data Lake Data-driven 94

Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again. Subscribe to Alation's Blog.

Big Data 95

Big Data Digital Transformation Data Lake Data-driven 95

Cloudera

JUNE 13, 2024

After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies. We hope to see you there.

Big Data 103

Big Data Machine Learning Contextual Data Data Lake 103

Cloudera

DECEMBER 1, 2022

Data observability provides insight into the condition and evolution of the data resources from source through the delivery of the data products. Barr Moses of Monte Carlo presents it as a combination of data flow, data quality, data governance, and data lineage. Source: IDC .

Data Quality 84

Data Quality Metrics Data Lake Statistics 84

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.

Data Lake 83

Data Lake Data Architecture Data-driven Data Warehouse 83

Alation

JANUARY 13, 2022

Thoughtworks says data mesh is key to moving beyond a monolithic data lake. Spoiler alert: data fabric and data mesh are independent design concepts that are, in fact, quite complementary. Thoughtworks says data mesh is key to moving beyond a monolithic data lake 2. Gartner on Data Fabric.

Data Lake 59

Data Lake Metadata Data-driven Data Governance 59

AWS Big Data

APRIL 4, 2024

Griffin is an open source data quality solution for big data, which supports both batch and streaming mode. In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical.

Data Quality 70

Data Quality Data Lake Data Warehouse Data-driven 70

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

SEPTEMBER 9, 2022

analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support AI, BI, ML, and data engineering on a single platform.” According to Gartner, Inc.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

erwin

DECEMBER 13, 2018

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices. To stay up to date, click here to subscribe.

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

DataKitchen

NOVEMBER 18, 2022

In addition to the tracking of relationships and quality metrics, DataOps Observability journeys allow users to establish baselines?concrete concrete expectations for run schedules, run durations, data quality, and upstream and downstream dependencies. And she’ll know when newer data will arrive.

Testing 130

Testing Statistics Measurement Dashboards 130

IBM Big Data Hub

APRIL 27, 2022

Since its uniquely metadata-driven, the abstraction layer of a data fabric makes it easier to model, integrate and query any data sources, build data pipelines, and integrate data in real-time. This improves data engineering productivity and time-to-value for data consumers. What’s a data mesh?

Management 79

Management Metadata Data Architecture Data Lake 79

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Data Virtualization

JANUARY 21, 2025

However, companies are still struggling to manage data effectively, to implement GenAI applications that deliver proven business value. The post OReilly Releases First Chapters of a New Book about Logical Data Management appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 52

Management Data Integration Technology Data Warehouse 52

erwin

AUGUST 31, 2023

Improved Decision Making : Well-modeled data provides insights that drive informed decision-making across various business domains, resulting in enhanced strategic planning. Reduced Data Redundancy : By eliminating data duplication, it optimizes storage and enhances data quality, reducing errors and discrepancies.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Ontotext

FEBRUARY 12, 2024

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Only metadata will be regenerated.

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

Cloudera

SEPTEMBER 6, 2022

Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing data lakes . Optimization Data lakehouse is the platform wherein the data assets reside. The post Modern Data Architecture for Telecommunications appeared first on Cloudera Blog.

Data Architecture 84

Data Architecture Cost-Benefit Digital Transformation Business Driver 84