Name: Data observability for Hadoop (CDH/HDP)
Start: 2022-11-08T18:54:00Z
End: 2022-11-08T18:54:54.000Z
Location: BrightTALK
Rating: 0

We'll cover aspects of data observability including best practices and tips to help data engineers, interviews with data executives, digital transformation and more.

To deliver the best business outcomes, you must continually monitor the accuracy, consistency, and completeness of your data—in other words, you must ensure that the data in your lakehouses and other mission-critical platforms is reliable.

Data observability enables real-time monitoring of the pipelines that ingest and prepare the data being loaded into these operational platforms. Having all this real-time intelligence at your users’ fingertips enables you to proactively monitor, diagnose, and remediate data issues.

Join this webinar for a discussion of how data observability can boost your data reliability. TDWI’s James Kobielus and panelists will discuss the following key questions:

What is data observability?
What are the principal data reliability metrics that you should use data observability to monitor and manage?
How should your organization deploy data observability to monitor and manage data reliability at scale on the lakehouse and throughout a diverse data ecosystem?
How can you use data observability to identify reliability issues at the source?
How should chief data officers, data engineers, data scientists, data analysts, and other stakeholders collaborate to ensure data reliability through robust monitoring and observability?

Using Lakehouse Monitoring&Data Observability to Deliver Enterprise Reliability

Leading financial services organizations are leveraging the value of their data and creating data products that support differentiation and growth.

What’s the secret?  

They have operationalized data reliability and data governance - enabling them to proactively improve customer experience, avoid compliance violations, and deliver highly reliable data at scale with predictable costs.

Join Chris Stephens, former CDO, Geico and John Morrell, Senior Director of Product Marketing, as they review strategies, tactics and best practices on how to automate and operationalize data reliability including:

Ways to get ahead of data 'blind spots' that cost 10X to fix when discovered 'downstream'
How fundamental trust in your data needs to be prioritized across any technical plans, architectural patterns or operating models
How data observability provides cost-effective operational and defensible metrics for data trust across an organization
How organizations can ensure that AI and LLM models are trained using reliable data

The Reason Why Data Processes Fail in Financial Services

Learn how Acceldata delivers deep insights into cloud cost optimization with its comprehensive data observability platform. Discover how to manage costs and maximize data ROI in platforms like Snowflake and Databricks by optimizing your spend, eliminating cost overruns, and allocating resources effectively.

Review how you can:

Enjoy 360-degree visibility into your spend and utilization across your end-to-end data stack through granular insights into costs by clusters/warehouse, workloads, users/account and business units.
Empower cost control and reduction, get actionable insights through anomaly detection and ML, and automate daily tasks such as administration, resource tuning, and workload optimization.
Create precise data spend plans through accurate budgeting, chargeback, fund allocation and spend forecasting at granular department- and project-levels.

How to optimize costs across your entire data stack using spend intelligence

CSPs have been striving to improve the quality, availability and usability of their data for many years. But the evolution of IT environments and partnerships are combining to make that task even more challenging. 

Data has always been spread across multiple applications and systems but now it is also spread across multiple cloud environments. This makes the task of identifying where issues, or problems, may lie with the data incredibly difficult. 

Watch the webinar to learn: 

How to track and manage costs in a multi-cloud environment
How and where data platforms need to scale to leverage the potential of data and AI/ML use cases 
The hidden costs of data quality and reliability 
The concept of data observability – what it means and what it delivers. 
Organizational structure and governance to deliver an optimal data platform and strategy

TMForum & T-Mobile: Building a data platform for CSPs' analytics ambitions

John Morrell, Sr. Dir. Product Marketing and Chris Cozzi, Dir, Product Management review new platform capabilities that deliver key business outcomes:

Spend Intelligence - control and optimize your cloud data warehouse spend with usage guardrails, automated health checks, chargeback and budgeting, automated shut offs of anomalous queries and more

Data Reliability - get end-to-end visibility into your pipeline and lineage, debug data delays at their root, leverage automated data policies to scale data quality with no code options

Operational Intelligence - prevent data outages, leverage automated actions to remediate issues, leverage recommendations for optimizing resource allocation and performance tuning

Webinar: What's New In Data Observability

Legacy data platforms are simply unable to manage the volume of data and provide the analytics and business insights to help build and manage data products.  To remain competitive, enterprises, must modernize their legacy data platforms. 

The session will cover how data observability addresses key modernization challenges such as:

De-risking data platform migrations with data reconciliation and pipeline performance comparison, both pre- and post-migration

Accelerating migration initiatives by automating and simplifying many aspects of validation and troubleshooting data and performance issues


Optimizing for cost by analyzing resource consumption to avoid unexpected overruns and determine optimal workload placement

Webinar: It's 2023. Time to Modernize Your Data Platform

As businesses make greater use of analytics throughout their organizations, care must be taken to ensure the right data is being used at the right time. This has elevated the need for data reliability to new levels. In this video, RTInsights’ Helen Beal discusses the topic with John Morrell, senior director of product marketing at Acceldata.

Build Better Data Products with Data Reliability

Building great data and analytics products requires a rethink and redo of common data management and analytics practices and tools. 

Fostering collaboration between product and business leaders and data teams, prioritizing data investments with an eye to the future such as adopting data observability helps create lasting competitive advantage. 
Data observability has now become essential to support and augment existing and modern data management architectures. 

Nancy Colton, Staff VP Technology, Enterprise Data & Analytics, Carelon (formerly Anthem) and Krishna Tummalapalli, Director, Data Engineering & BI, Ohio Health discuss the criticality of data products to the healthcare industry, common data challenges, and the benefits of incorporating data observability into data strategies.

Webinar: Build Great Healthtech Data Products Using Data Observability

Acceldata for Kafka uniquely supports monitoring, insights and automation to ensure data quality, reliability, performance and scale for Kafka-based environments. Our platform ensures data reliability as data is streaming into Kafka, and accurate data reconciliation as data moves from Kafka to other destinations.

Data Observability for Kafka

Acceldata is the only data observability solution that delivers actionable insights and proactive notifications to improve spend forecasting, data reliability, and reduce wasteful practices in Databricks and Snowflake. This webinar covers:

Spend forecasting, projected spend analysis, department-level tracking and chargebacks
How to detect data quality and reliability within tables
How to quickly detect over provisioning, unused resources & warehouse load
Identify toot cause analysis, historical comparison, bottleneck analysis, and more…

Product Overview: Data Observability Cloud

Everyone is collecting data, but few companies know how to get the most value from it.  Between collection and complex analysis, there are many stages to data management — which means many opportunities for something to go wrong. According to Gartner, poor quality data costs organizations an average of $12.8 million per year. Minimizing this drain on resources will require improving data operations, through data observability. Fortunately, the right data management system will also reap significant business benefits.

The first stage of successful data ops is gaining a comprehensive understanding of what’s going on in your data environment, both the good and the bad. In this first installment of the Acceldata series “Turning Data into Information: How Multi-dimensional Data Observability Uncovers the Insights at Your Fingertips,” data observability experts will dig deep into the data pipeline, data and data processing layers and call out the pain points you may not even be aware of.

Join Acceldata and a panel of experts to learn more about:
Why data collection isn’t enough
How to re-envision your data pipeline as a supply chain, with each stage leaving its mark on the larger data lifecycle
What companies most commonly get wrong about their data quality strategy
The opportunities available if you commit to a data observability protocol

Demystifying Data 101: The Nuts and Bolts of Successful Data Ops

Acceldata has developed the world's first multidimensional Data Observability Cloud to help enterprises transform their data systems from unreliable, hard-to-scale, and expensive to stable, agile, and cost efficient. Acceldata’s platform observes the data processing, data quality, and data pipeline layers of modern data stacks, giving insight, optimization and scalable options to data teams as data traverses from ingestion to consumption through complex interconnected data systems.

Acceldata: Multidimensional data observability overview demo

Acceldata’s data observability solution for Hadoop (CDH/HDP) offers a software and support solution that offers 50% support savings over your existing provider, and a 30% increase in operational efficiency. With this solution you’re not forced to implement an expensive and time consuming upgrade. You also get full break/fix & patching support from some of the world’s foremost experts in Hadoop and best-in-class resolution SLAs.

Data observability for Hadoop (CDH/HDP)

Apache Hadoop

Cloudera

Data Observability

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Data observability for Hadoop (CDH/HDP)

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