AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

Metadata 77

Metadata Testing Data Lake Consulting 77

Cloudera

MARCH 17, 2023

When implementing a data lakehouse, the table format is a critical piece because it acts as an abstraction layer, making it easy to access all the structured, unstructured data in the lakehouse by any engine or tool, concurrently. Also, the data lake layer is not limited to cloud object stores.

Unstructured Data 61

Unstructured Data Data Lake Data Warehouse Machine Learning 61

erwin

FEBRUARY 28, 2024

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

Metadata 52

Metadata Data Governance Data Quality Technology 52

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

datapine

SEPTEMBER 29, 2022

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? Industry-wide, the positive ROI on quality data is well understood.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

DECEMBER 21, 2022

Transactional systems and data warehouses can then use the golden records as the entity’s most current, trusted representation. Data Catalog and Master Data Management. Documenting rule definitions and corrective actions guide domain owners and stewards in addressing quality issues.

Management 52

Management Data Quality Metadata Testing 52

erwin

OCTOBER 31, 2019

What data do we have and where is it? Data is a critical asset used to operate, manage and grow a business. While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

Cloudera

JANUARY 19, 2024

This can include assumptions about the intent of the natural language used, like the definition of “top selling products,” values of needed literals, and how joins can be created. Log in to the Cloudera Data Warehouse service as DWAdmin. Log in to the data warehouse service as DWAdmin.

Data Warehouse 71

Data Warehouse Data Processing Optimization Modeling 71

Octopai

DECEMBER 10, 2019

It should go beyond just listing definitions to key terms. Those terms include entry data, authorship information and they should be interlinked so anyone who looks at it can gain a full understanding of the processes being described.

Metadata 53

Metadata Data Warehouse Reporting Business Intelligence 53

Cloudera

SEPTEMBER 15, 2022

The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.

Data Lake 79

Data Lake Data Architecture Data-driven Data Warehouse 79

Octopai

MARCH 24, 2020

Let’s start with automated tools that foster the seamless interaction of multiple metadata best practices, such as data discovery, data lineage and the use of a business glossary. Here is an overview of how automated metadata management makes your business intelligence smarter. How Can Smarter BI Impact Your Company?

Metadata 43

Metadata Cost-Benefit Business Intelligence ROI 43

Alation

FEBRUARY 13, 2020

These are all good questions and a logical place to start your data cataloging journey. This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. If you want more control over and more value from all your data, join us for a demo of erwin MM.

Data Governance 40

Data Governance Risk Metadata Management 40

Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

erwin

MAY 2, 2022

Data modeling is a serious scientific method with many rules and best practices. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. I was pricing a data warehousing project with just 4 TB of data – small by today’s standards. Figure 11: Data normalization.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

AWS Big Data

OCTOBER 4, 2023

Business-driven domains – A DataZone domain represents the distinct boundary of a line of business (LOB) or a business area within an organization that can manage its own data, including its own data assets, its own definition of data or business terminology, and may have its own governing standards.

Metadata 100

Metadata Data Lake Publishing Data Governance 100

Cloudera

AUGUST 30, 2018

How do I get to the next level in the data-driven journey fast enough? How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets? At Cloudera, we believe a strong partnership and the right technology foundation can put you on the path to data-driven success. Tough decisions.

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

Ontotext

DECEMBER 15, 2022

But whatever their business goals, in order to turn their invisible data into a valuable asset, they need to understand what they have and to be able to efficiently find what they need. Enter metadata. It enables us to make sense of our data because it tells us what it is and how best to use it. Knowledge (metadata) layer.

Metadata 52

Metadata Unstructured Data Structured Data Enterprise 52

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Definition and Descriptions.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

Metadata 93

Metadata Management Data Quality Data Governance 93

erwin

FEBRUARY 7, 2019

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. The volume and variety of data has snowballed, and so has its velocity. As such, traditional – and mostly manual – processes associated with data management and data governance have broken down.

Metadata 96

Metadata Insurance Data-driven Cost-Benefit 96

Alation

JANUARY 26, 2023

Offer the right tools Data stewardship is greatly simplified when the right tools are on hand. So ask yourself, does your steward have the software to spot issues with data quality, for example? Do they have a system to manage the metadata for given assets? One example is the EU’s General Data Protection Regulation (GDPR).

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map.

Data Lake 92

Data Lake Data Warehouse Metadata Data Architecture 92

Alation

OCTOBER 18, 2022

In the modern data stack, dbt is a key tool to make data ready for analysis. Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Improve data analysis accuracy.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. Wait for the crawler to complete.

Data Lake 92

Data Lake Snapshot Metadata Optimization 92

Cloudera

FEBRUARY 8, 2024

Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables. release and the matching CDW Private Cloud Data Services release, Hive also supports creating, using, and rebuilding materialized views for Iceberg table format.

Snapshot 88

Snapshot Metadata Cost-Benefit Data Warehouse 88

Ontotext

MARCH 8, 2023

Rich metadata and semantic modeling continue to drive the matching of 50K training materials to specific curricula, leading new, data-driven, audience-based marketing efforts that demonstrate how the recommender service is achieving increased engagement and performance from over 2.3 million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. But first, let’s start with basic definitions. One of the BI architecture components is data warehousing. What Is Data Warehousing And Business Intelligence? Data integration. Data storage.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

IBM Big Data Hub

OCTOBER 4, 2023

In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022. New insights and relationships are found in this combination. All of this supports the use of AI.

Data Lake 85

Data Lake Metadata Cost-Benefit Data Warehouse 85

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker.

Data Lake 90

Data Lake Data Processing Metadata Snapshot 90

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 64

Data Architecture Metadata Data Lake Snapshot 64

AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. Data providers and consumers are the two fundamental users of a CDH dataset.

Dashboards 94

Dashboards Analytics Metadata Data Warehouse 94

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52