AWS Big Data

NOVEMBER 15, 2023

For more information on this foundation, refer to A Detailed Overview of the Cost Intelligence Dashboard. 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.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

AWS Big Data

AUGUST 23, 2023

It uses Amazon Simple Storage Service (Amazon S3) as the primary data storage for indexes, adding durability for your data. Collections are able to take advantage of the S3 storage layer to reduce the need for hot storage, and reduce cost, by bringing data into local store when it’s accessed. in OpenSearch Service).

Snapshot 95

Snapshot Dashboards Visualization Metrics 95

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

FineReport

FEBRUARY 5, 2020

Based on the study of the evaluation criteria of Gartner Magic Quadrant for analytics and Business Intelligence Platforms, I have summarized top 10 key features of BI tools for your reference. Overall, as users’ data sources become more extensive, their preferences for BI are changing. Metadata management. Analytics dashboards.

Metadata 98

Metadata Dashboards Informatics Visualization 98

AWS Big Data

JULY 3, 2023

Major market indexes, such as S&P 500, are subject to periodic inclusions and exclusions for reasons beyond the scope of this post (for an example, refer to CoStar Group, Invitation Homes Set to Join S&P 500; Others to Join S&P 100, S&P MidCap 400, and S&P SmallCap 600 ). Load the dataset into Amazon S3.

Snapshot 86

Snapshot Data Lake Testing Strategy 86

Ontotext

MAY 5, 2023

It is reused in modeling the publication of entity data or regulatory-mandated data exchange, as seen in the example provided below. Integrating reporting to move to a more streamlined, efficient approach to data collection. We think their adoption will bring benefits well beyond reporting.

Data Collection 64

Data Collection Risk Data-driven Interactive 64

Alation

FEBRUARY 20, 2020

More than any other advancement in analytic systems over the last 10 years, Hadoop has disrupted data ecosystems. By dramatically lowering the cost of storing data for analysis, it ushered in an era of massive data collection.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Alation

FEBRUARY 20, 2020

earthquake, flood, or fire), where the data collected does not need to be as tightly controlled. Since an earthquake event can generate gigabytes of data, a company can spin up extra computing nodes, process the data, and spin down the nodes once the processing is complete. In The Alation Data Catalog adding S3 is simple.

Data Lake 52

Data Lake ROI Metadata Cost-Benefit 52

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Maybe they analyzed the metadata from pictures and found that there was a strong correlation between properties that rented often and expensive camera models.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Ontotext

MAY 18, 2020

Exascale computing refers to systems capable of at least one exaFLOPS calculation per second and that is billion billion (or if you wish a quintillion) operations per second. There are four types of data sources that the team will work with. The first type is metadata from images. Certainly not!

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Sisense

JULY 6, 2020

First off, this involves defining workflows for every business process within the enterprise: the what, how, why, who, when, and where aspects of data. These regulations, ultimately, ensure key business values: data consistency, quality, and trustworthiness.

Enterprise 98

Enterprise Management Data Architecture Strategy 98

Juice Analytics

FEBRUARY 21, 2018

This means that two different rows in the data can represent the same entity with data collected for it at different points in time. As a consequence of the rule above, the data should include a row identifier column that can be repeated to indicate that different rows of data are representing the same entities.

Metadata 42

Metadata Metrics Data Collection Reporting 42

Alation

JANUARY 17, 2023

Data would be pulled from various sources, organized into, say, a table, and loaded into a data warehouse for mass consumption. This was not only time-consuming, but the growing popularity of cloud data warehouses compelled people to rethink this process. 2) Get a data ingestion tool and connect your data sources.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Andrew White

JANUARY 9, 2022

Where these efforts break down is in the data that goes into the connection at one end and comes out the other. So, I hear you say, let’s share metadata and make the data self-describing. Here is a suggested note: Use Gartner’s Reference Model to Deliver Intelligent Composable Business Applications.

Analytics 64

Analytics Measurement Modeling Data-driven 64

The Unofficial Google Data Science Blog

SEPTEMBER 9, 2015

Record-level program scope As a data scientist, you write a Sawzall script to operate at the level of a single record. The scope of each record is determined by the source of the data; it might be a web page, metadata about an app, or logs from a web server. References [1] Interpreting the Data: Parallel Analysis in Sawzall.

Data Science 56

Data Science Statistics Testing Metadata 56

datapine

JANUARY 31, 2022

BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

NOVEMBER 11, 2021

Let’s take a look at some of the key principles for governing your data in the cloud: What is Cloud Data Governance? Cloud data governance is a set of policies, rules, and processes that streamline data collection, storage, and use within the cloud. This framework maintains compliance and democratizes data.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

erwin

JULY 17, 2019

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more.

Testing 130

Testing Data Governance Data Quality Data-driven 130

O'Reilly on Data

DECEMBER 19, 2018

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

Machine Learning 261

Machine Learning Software Metadata Testing 261

Cloudera

NOVEMBER 22, 2021

Explosion of data availability from a variety of sources, including on-premises data stores used by enterprise data warehousing / data lake platforms, data on cloud object stores typically produced by heterogenous, cloud-only processing technologies, or data produced by SaaS applications that have now evolved into distinct platform ecosystems (e.g.,

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88

Jet Global

MAY 1, 2023

that gathers data from many sources. Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns. It’s all about context.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52