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How Much Should I Be Spending On Observability?

Honeycomb

All Gartner data in this piece was pulled from this webinar on cost control ; slides here.) download Model-specific cost drivers: the pillars model vs consolidated storage model (observability 2.0) Because the cost drivers of the multiple pillars model and unified storage model are very different. and observability 2.0.

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The top 15 big data and data analytics certifications

CIO

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. CDP Generalist The Cloudera Data Platform (CDP) Generalist certification verifies proficiency with the Cloudera CDP platform.

Big Data 190
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What’s new in CDP Private Cloud 1.2?

Cloudera

Yet for organizations that only want to get their toes wet and perhaps just evaluate the capability, the 16 cores, 128 GB RAM, and 600 GB of storage prevented them from doing just that. we introduce detailed low resource requirements that reduce the amount of CPU, RAM, and storage needed by up to 75%. With Private Cloud 1.2,

Cloud 90
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Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

Agencies are plagued by a wide range of data formats and storage environments—legacy systems, databases, on-premises applications, citizen access portals, innumerable sensors and devices, and more—that all contribute to a siloed ecosystem and the data management challenge. . That’s just the tip of the iceberg.

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Five Trends for 2019

Hu's Place - HitachiVantara

Data curation will be a focus to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need to power the organization.

Trends 86
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An Overview of Real Time Data Warehousing on Cloudera

Cloudera

It has the key elements of fast ingest, fast storage, and immediate querying for BI purposes. Basic Architecture for Real-Time Data Warehousing. These include stream processing/analytics, batch processing, tiered storage (i.e. for active archive or joining live data with historical data), or machine learning.

Data 97
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The Future of the Data Lakehouse – Open

Cloudera

Delta lake had a Spark-heavy evolution; customer options dwindle rapidly if they need freedom to choose a different engine than what is primary to the table format. . More formats, more engines, more interoperability. Today, the Hive metastore is used from multiple engines and with multiple storage options.

Data 94