Name: Reliable Data Pipelines 101
Start: 2022-01-04T17:58:00Z
End: 2022-01-04T17:58:20.000Z
Location: BrightTALK
Rating: 0

Bigeye’s mission is to make it effortless for data teams to measure, improve, and communicate data quality for their organizations. Follow the channel for the latest trends and best practices related to data quality, data engineering, and data science. 

Companies increasingly rely on data to move the business forward — making accurate analytics critical, but addressing the data quality issues that spoil analytics isn’t a one-sized fit all problem. As analytics teams move from producing reports to building data products to enabling stakeholders to build their own data products, they must change how they approach data quality. 

In this presentation, originally presented at Strata Streaming Business Analysis, Kyle Kirwan, CEO and co-founder of Bigeye, describes how to evolve data quality practices to ensure high-quality decision-making throughout each stage of the self-service journey.

Accuracy in analytics: Trusted data in a self-service world

The automation that AI and ML provide has been widely seen as a solution to dealing with the complex nature of real-world data. Companies have rushed to take advantage of AI and ML to supercharge their businesses. Yet, most AI and ML initiatives fail. Why? Because data scientists aren’t effectively exploring the data.

Data scientists are bogged down by huge volumes of data in increasingly dynamic environments without the tools to effectively explore their data. As a result, shifting patterns in the data erode even the highest performing pipelines.

In this webinar, Henry Li, senior data scientist at Bigeye and former data scientist at Uber, will discuss the importance of exploratory data analytics for AI and ML pipelines, how it’s done, why it’s a struggle for most data teams, and how data monitoring can help.

Join this webinar to learn:
Why exploratory data analysis keeps so many companies from making the leap to being data driven
How it affects the day-to-day work of data scientists
How Bigeye can help by giving data teams have complete awareness of what’s happening with their data at all times

Why AI and ML Pipelines Fail: The Exploratory Data Analysis Problem

Anyone who has spent time working with data has seen the negative effects of data quality. Data quality incidents slow analysis to a crawl, damage executive dashboards, break user-facing applications, and impact machine learning model performance. Data quality is a big problem and attempting to tackle it all at once can make it hard to show meaningful results.

In this webinar, Egor Gryaznov, CTO and co-founder of Bigeye, will discuss a three-phase approach to addressing data quality, including how to put in place a solid toolchain and process for showing traction at each phase.

Join this webinar learn a three-phase approach to addressing data quality, including:

- An in-depth look at the three phases of data quality: operational quality, logical quality, and application quality.
- A grasp of the toolchain and process needed to address each phase of data quality
- A look at how Bigeye can help address operational data quality and more

How to Tackle Data Quality: A Three-Phase Approach

Whether it’s for an executive dashboard or an ML model, reliable data is critical for the applications that make data-driven companies tick. But when it comes to creating reliable data pipelines, where do you start and what tools and processes do you need in place? 

Egor Gryaznov is the co-founder and CTO of Bigeye and was one of the first data engineers at Uber. Egor will draw from his experience supporting thousands of internal users and mission-critical workloads at Uber to provide an actionable guide to data pipeline reliability. 

In this presentation, you will learn:

- How to approach building data pipelines for a data application
- What tools you will encounter in development and what you need to know about each
- How to create SLAs to better align with stakeholders
- How to manage the data that your application creates

Reliable Data Pipelines 101

data pipelines

data reliability

Data Quality

data engineering

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