AWS Big Data

MARCH 27, 2023

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. Tags allows you to assign metadata to your AWS resources. Analytics Specialist based out of Northern Virginia, specialized in the design and implementation of analytics and data lake solutions.

Data Warehouse 84

Data Warehouse Management Snapshot Data Lake 84

AWS Big Data

FEBRUARY 24, 2023

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. A data lake built on AWS uses Amazon Simple Storage Service (Amazon S3) as its primary storage environment.

Data Lake 83

Data Lake Metadata Testing Data Warehouse 83

Ontotext

APRIL 10, 2024

In most enterprises data teams lack a data map and data asset inventory and are often unaware of data that exists across the organization, its associated profile, quality and associated metadata. Teams can’t access data to build their business use cases. For example, a product data tag is basic metadata.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Introduction.

Data Warehouse 90

Data Warehouse Cost-Benefit Consulting Interactive 90

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

AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 94

Data Governance Unstructured Data Metadata Data Lake 94

CIO Business Intelligence

JUNE 23, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 98

Data Lake Data Warehouse Machine Learning Cost-Benefit 98

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 89

Data Lake Data Warehouse Machine Learning Cost-Benefit 89

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 112

Data Lake Analytics Dashboards Metrics 112

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.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

AWS Big Data

JANUARY 24, 2024

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. It will never remove files that are still required by a non-expired snapshot.

Snapshot 99

Snapshot Data Lake Metadata Optimization 99

CIO Business Intelligence

OCTOBER 13, 2023

Data architect Armando Vázquez identifies eight common types of data architects: Enterprise data architect: These data architects oversee an organization’s overall data architecture, defining data architecture strategy and designing and implementing architectures.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 71

Analytics Data Warehouse Data Lake Metadata 71

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy.

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AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 83

Enterprise Data Warehouse Snapshot Cost-Benefit 83

CIO Business Intelligence

MAY 24, 2022

The rules are part of what the company calls the Data Quality Accelerator for Financial Services and can be used to accelerate the deployment of a data project and enable data-driven decision making. . Cloud Computing, Data Management, Financial Services Industry, Healthcare Industry

Finance 140

Finance Management Metadata Data Quality 140

AWS Big Data

JULY 20, 2023

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table.

Data Lake 80

Data Lake Metadata Data Processing Big Data 80

Cloudera

MARCH 17, 2023

The table format provides the necessary structure for the unstructured data that is missing in a data lake, using a schema or metadata definition, to bring it closer to a data warehouse. Some of the popular table formats are Apache Iceberg, Delta Lake, Hudi, and Hive ACID.

Unstructured Data 60

Unstructured Data Data Lake Data Warehouse Machine Learning 60

AWS Big Data

JANUARY 17, 2024

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

Data Lake 93

Data Lake Snapshot Big Data Data-driven 93

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 79

Metadata Testing Data Lake Consulting 79

TDAN

MAY 31, 2022

As we enter a new cloud-first era, advancements in technology have helped companies capture and capitalize on data as much as possible. Deciding between which cloud architecture to use has always been a debate between two options: data warehouses and data lakes.

Data Lake 52

Data Lake Data Warehouse Technology Data Architecture 52

AWS Big Data

DECEMBER 5, 2023

A combination of Amazon Redshift Spectrum and COPY commands are used to ingest the survey data stored as CSV files. For the files with unknown structures, AWS Glue crawlers are used to extract metadata and create table definitions in the Data Catalog. Outside of work she enjoys traveling and trying new cuisines.

Measurement 82

Measurement Dashboards Data Warehouse Analytics 82

AWS Big Data

JANUARY 31, 2023

We have collected some of the key talks and solutions on data governance, data mesh, and modern data architecture published and presented in AWS re:Invent 2022, and a few data lake solutions built by customers and AWS Partners for easy reference.

Data Lake 81

Data Lake Data Governance Data Architecture Machine Learning 81

Alation

FEBRUARY 13, 2020

Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. When speaking and consulting, I often hear people refer to data in their data lakes and data warehouses as curated data, believing that it is curated because it is stored as shareable data.

Metadata 52

Metadata Data Warehouse Data Lake Data Governance 52

Cloudera

JANUARY 19, 2022

Cloudera Data Platform (CDP) scored among the top 10 vendors on all four Analytical Use Cases — Data Warehouse, Logical Data Warehouse, Data Lake and Operational Intelligence in the Critical Capabilities for Cloud Database Management Systems for Analytics Use Cases.

Reporting 86

Reporting Data Warehouse Data Lake Machine Learning 86

Timo Elliott

DECEMBER 14, 2022

Most innovation platforms make you rip the data out of your existing applications and move it to some another environment—a data warehouse, or data lake, or data lake house or data cloud—before you can do any innovation.

Data Lake 105

Data Lake Recreation/Entertainment Metadata Data Warehouse 105

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. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 77

Risk Modeling Management Metadata 77

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

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

Timo Elliott

JUNE 19, 2023

The next area is data. There’s a huge disruption around data. For a long time, we’ve always ripped data out of our core systems and put it into a data warehouse or a data lake or a data lake house or a data cloud. It works, but it’s a lot of hard work.

Data Lake 105

Data Lake Digital Transformation Recreation/Entertainment Technology 105

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

TDAN

MARCH 19, 2019

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.

Management 84

Management Data Lake Data Warehouse Analytics 84

AWS Big Data

MARCH 25, 2024

Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently. With a file system sink connector, Apache Flink jobs can deliver data to Amazon S3 in open format (such as JSON, Avro, Parquet, and more) files as data objects.

Data Lake 89

Data Lake Unstructured Data Management Modeling 89

Andrew White

DECEMBER 9, 2019

The client had recently engaged with a well-known consulting company that had recommended a large data catalog effort to collect all enterprise metadata to help identify all data and business issues. Modern data (and analytics) governance does not necessarily need: Wall-to-wall discovery of your data and metadata.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

AWS Big Data

SEPTEMBER 13, 2023

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. Of those tables, some are larger (such as in terms of record volume) than others, and some are updated more frequently than others.

Data Lake 90

Data Lake Data Processing Metadata Snapshot 90

Ontotext

NOVEMBER 16, 2023

In a data mesh, domains are represented by a node, which can be an operational data store (ODS), a data warehouse, or a data lake tailored to the domain’s requirements. Mesh emerges when teams use other domains’ data products and the domains communicate with others in a governed manner.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52