Name: Data Observability for Kafka
Start: 2022-11-08T18:44:00Z
End: 2022-11-08T19:32:48.000Z
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.

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’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)

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 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

Data Observability

Data Management

Apache Kafka

Data Quality

Data Reliability

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IT Governance, Risk and Compliance

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Data Observability for Kafka

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