CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Meet the data lakehouse.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

CIO Business Intelligence

MAY 24, 2022

The Intelligent Data Management Cloud for Financial Services, like Informatica’s other industry-focused platforms, combines vertical-based accelerators with the company’s suite of machine learning tools to help with challenges around unstructured data and quick data-based decision making. .

Finance 140

Finance Management Metadata Machine Learning 140

David Menninger's Analyst Perspectives

OCTOBER 31, 2024

It was not until the addition of open table formats— specifically Apache Hudi, Apache Iceberg and Delta Lake—that data lakes truly became capable of supporting multiple business intelligence (BI) projects as well as data science and even operational applications and, in doing so, began to evolve into data lakehouses.

Data Lake 130

Data Lake Unstructured Data Data Warehouse Software 130

CIO Business Intelligence

APRIL 25, 2024

Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work. It works in Salesforce just like any other native Salesforce data,” Carlson said.

Data Integration 119

Data Integration Data Lake Data Warehouse Metadata 119

Smart Data Collective

SEPTEMBER 26, 2021

Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. It’s a good idea to record metadata.

Metadata 130

Metadata Visualization Unstructured Data Data mining 130

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

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

Smart Data Collective

OCTOBER 13, 2021

There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement. How does Data Virtualization complement Data Warehousing and SOA Architectures?

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

Data Lake 137

Data Lake Data Processing Metadata Snapshot 137

AWS Big Data

JUNE 26, 2024

New feature: Custom AWS service blueprints Previously, Amazon DataZone provided default blueprints that created AWS resources required for data lake, data warehouse, and machine learning use cases. You can build projects and subscribe to both unstructured and structured data assets within the Amazon DataZone portal.

Data Lake 118

Data Lake Data Warehouse Unstructured Data Data Governance 118

Cloudera

APRIL 1, 2024

We scored the highest in hybrid, intercloud, and multi-cloud capabilities because we are the only vendor in the market with a true hybrid data platform that can run on any cloud including private cloud to deliver a seamless, unified experience for all data, wherever it lies.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model? What is a data vault?

Data Warehouse 91

Data Warehouse Data Lake Cost-Benefit Metadata 91

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 122

Data Lake Sales Data Warehouse Snapshot 122

Cloudera

SEPTEMBER 9, 2022

Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Less data gets decompressed, deserialized, loaded into memory, run through the processing, etc.

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

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. Some of the popular table formats are Apache Iceberg, Delta Lake, Hudi, and Hive ACID.

Unstructured Data 64

Unstructured Data Data Lake Data Warehouse Machine Learning 64

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

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 Data Warehouse 66

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. Frequent table maintenance needs to be performed to prevent read performance from degrading over time.

Data Lake 130

Data Lake Metadata Statistics Optimization 130

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. For building such a data store, an unstructured data store would be best. versions).

Data Lake 121

Data Lake Unstructured Data Management Snapshot 121

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction.

Data Processing 94

Data Processing Data Warehouse Enterprise Visualization 94

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 131

Data Lake Analytics Dashboards Metrics 131

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The many data warehouse systems designed in the last 30 years present significant difficulties in that respect.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Cloudera

NOVEMBER 15, 2024

Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis. Cyber logs are often unstructured or semi-structured, making it difficult to derive insights from them.

Analytics 59

Analytics Metadata Data-driven Snapshot 59

Cloudera

SEPTEMBER 15, 2022

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The collection of source data shown on your left is composed of both structured and unstructured data from the organization’s internal and external sources.

Data Lake 83

Data Lake Data Architecture Data-driven Data Warehouse 83

AWS Big Data

JANUARY 8, 2024

Stream ingestion – The stream ingestion layer is responsible for ingesting data into the stream storage layer. It provides the ability to collect data from tens of thousands of data sources and ingest in real time.

Analytics 134

Analytics IoT Data-driven Snapshot 134

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 Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

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. Technical metadata to describe schemas, indexes and other database objects.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Alation

APRIL 6, 2023

Other forms of governance address specific sets or domains of data including information governance (for unstructured data), metadata governance (for data documentation), and domain-specific data (master, customer, product, etc.). Data catalogs and spreadsheets are related in many ways.

Data Governance 52

Data Governance Metadata Cost-Benefit Structured Data 52

Sisense

MAY 28, 2020

“Not only do they have to deal with data that is distributed across on-premises, hybrid, and multi-cloud environments, but they have to contend with structured, semi-structured, and unstructured data types. That’s without mentioning outdated metadata—the data about data that provides data intelligence,” said Gopal.

Internet of Things 83

Internet of Things Metadata Data-driven Unstructured Data 83

Wayne Yaddow

MARCH 14, 2025

Workaround: Implement custom metadata tracking scripts or use dbt Clouds freshness monitoring. Testing Limitations: Both dbt Cloud and dbtCore dbt is designed for SQL-based transformations in data warehouses, meaning it is not well-suited for non-SQL, real-time, or highly complex unstructured data transformations.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

Cloudera

AUGUST 21, 2020

Perhaps one of the most significant contributions in data technology advancement has been the advent of “Big Data” platforms. Historically these highly specialized platforms were deployed on-prem in private data centers to ensure greater control , security, and compliance. But the “elephant in the room” is NOT ‘Hadoop’.

Cost-Benefit 76

Cost-Benefit Big Data ROI Risk 76

AWS Big Data

FEBRUARY 22, 2023

An AWS Glue ETL job, using the Apache Hudi connector, updates the S3 data lake hourly with incremental data. The AWS Glue job can transform the raw data in Amazon S3 to Parquet format, which is optimized for analytic queries. All the metadata of the tables is stored in the AWS Glue Data Catalog, including the Hudi tables.

Data Lake 52

Data Lake Dashboards Cost-Benefit Data Warehouse 52

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. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

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

Laminar Security

MAY 3, 2023

They define DSPM technologies this way: “DSPM technologies can discover unknown data and categorize structured and unstructured data across cloud service platforms. In it they provide recommendations for getting started with DSPM and important considerations for DSPM solutions.

Management 40

Management Risk Risk Management Data Processing 40

Cloudera

SEPTEMBER 17, 2020

To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle. This platform, including an ad-hoc capable data warehouse service with built-in, easy-to-use visualization, made it easy for anyone to jump in and start experimenting.

Data Warehouse 81

Data Warehouse Experimentation Dashboards Visualization 81