Observability for Data Pipelines With OpenLineage

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Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment. Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security. Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.

Data lineage and observability
with OpenLineage
Julien Le Dem, CTO and Co-Founder Datakin | Mai 2021

AGENDA
● The need for metadata
● OpenLineage - the open standard for lineage
collection - and Marquez, its reference
implementation
● Spark observability with OpenLineage

Building a healthy data ecosystem
Team A Team B
Team C

5
Today: Limited context
● What is the data source?
● What is the schema?
● Who is the owner?
● How often is it updated?
● Where is it coming from?
● Who is using the data?
● What has changed?
DATA

Maslow’s Data hierarchy of needs
New Business Opportunities
Business Optimization
Data Quality
Data Freshness
Data Availability

OpenLineage contributors
Creators and contributors from major open source projects involved

Purpose:
Deﬁne an Open standard for
metadata and lineage collection
by instrumenting data pipelines
as they are running.

Problem
Before:
● Duplication of effort: Each project
has to instrument all jobs
● Integrations are external and can
break with new versions
● Effort of integration is shared
● Integration can be pushed in
each project: no need to play
catch up
With Open Lineage

Open Lineage scope Not in scope
Backend
Integrations
Metadata
and
lineage
collection
standard
Warehouse
Schedulers
...
Kafka
topic
Graph
db
HTTP
client
Consumers
Kafka
client
GraphDB
client
...

Core Model:
- JSONSchema spec
- Consistent naming:
Jobs:
scheduler.job.task
Datasets:
instance.schema.table
13

14
Protocol:
- Asynchronous events:
Unique run id for identifying a
run and correlate events
- Configurable backend:
- Kafka
- Http
Examples:
● Run Start event
○ source code version
○ run parameters
● Run Complete event
○ input dataset
○ output dataset version and schema

15
Facets
● Extensible:
Facets are atomic pieces of metadata
identiﬁed by a unique name that can be
attached to the core entities.
● Decentralized:
Preﬁxes in facet names allow the
deﬁnition of Custom facets that can be
promoted to the spec at a later point.

Metadata:
Ingest Storage Compute
Streaming
Batch/ML
● Data Platform
built around
Marquez
● Integrations
○ Ingest
○ Storage
○ Compute
Flink
Airflow
Kafka
Iceberg / S3
BI
OpenLineage

Marquez: Data model
Job
Dataset Job Version
Run
*
1
*
1
*
1
1
*
1
*
Source
1 *
● MYSQL
● POSTGRESQL
● REDSHIFT
● SNOWFLAKE
● KAFKA
● S3
● ICEBERG
● DELTALAKE
● BATCH
● STREAM
● SERVICE
Dataset Version

API
● Open Lineage and Marquez standardize
metadata collection
○ Job runs
○ Parameters
○ Version
○ Inputs / outputs
● Datakin enables
○ Understanding operational dependencies
○ Impact analysis
○ Troubleshooting: What has changed
since the last time it worked?
Datakin leverages Marquez metadata
Lineage analysis
Graph
Integrations

22
Spark java agent
spark.driver.extraJavaOptions:
-javaagent:marquez-spark-0.13.1.jar={argument}

Metadata
collected
23
Lineage: inputs/outputs
Data volume: row count/byte size
Logical plan

26
Example of
OpenLineage
metadata usage:
Data volume
evolution

Join the conversation
OpenLineage:
Github: github.com/OpenLineage
Slack: OpenLineage.slack.com
Twitter: @OpenLineage
Email: groups.google.com/g/openlineage
Marquez:
Github: github.com/MarquezProject/marquez
Slack: MarquezProject.slack.com
Twitter: @MarquezProject

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The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

Democratizing Data Quality Through a Centralized Platform

Databricks

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Learn to Use Databricks for Data Science

Databricks

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Why APM Is Not the Same As ML Monitoring

Databricks

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Sawtooth Windows for Feature Aggregations

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Re-imagine Data Monitoring with whylogs and Spark

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Massive Data Processing in Adobe Using Delta Lake

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Machine Learning CI/CD for Email Attack Detection

Databricks

Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models. In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Databricks

Sarah: CEO-Finance-Report pipeline seems to be slow today. Why Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days. Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours. Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert. We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example: Tom: I am not seeing any data for today in my Campaign Metrics Dashboard. Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021. This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Databricks

This presentation introduces Tune and Fugue, frameworks for intuitive and scalable hyperparameter optimization (HPO). Tune supports both non-iterative and iterative HPO problems. For non-iterative problems, Tune supports grid search, random search, and Bayesian optimization. For iterative problems, Tune generalizes algorithms like Hyperband and Asynchronous Successive Halving. Tune allows tuning models both locally and in a distributed manner without code changes. The presentation demonstrates Tune's capabilities through examples tuning Scikit-Learn and Keras models. The goal of Tune and Fugue is to make HPO development easy, testable, and scalable.

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Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Machine Learning CI/CD for Email Attack Detection

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

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1. Data lineage and observability with OpenLineage Julien Le Dem, CTO and Co-Founder Datakin | Mai 2021

2. AGENDA ● The need for metadata ● OpenLineage - the open standard for lineage collection - and Marquez, its reference implementation ● Spark observability with OpenLineage

3. The need for Metadata 3

4. Building a healthy data ecosystem Team A Team B Team C

5. 5 Today: Limited context ● What is the data source? ● What is the schema? ● Who is the owner? ● How often is it updated? ● Where is it coming from? ● Who is using the data? ● What has changed? DATA

6. Maslow’s Data hierarchy of needs New Business Opportunities Business Optimization Data Quality Data Freshness Data Availability

7. OpenLineage 7

8. OpenLineage contributors Creators and contributors from major open source projects involved

9. Purpose: Deﬁne an Open standard for metadata and lineage collection by instrumenting data pipelines as they are running.

10. Purpose: EXIF for data pipelines

11. Problem Before: ● Duplication of effort: Each project has to instrument all jobs ● Integrations are external and can break with new versions ● Effort of integration is shared ● Integration can be pushed in each project: no need to play catch up With Open Lineage

12. Open Lineage scope Not in scope Backend Integrations Metadata and lineage collection standard Warehouse Schedulers ... Kafka topic Graph db HTTP client Consumers Kafka client GraphDB client ...

13. Core Model: - JSONSchema spec - Consistent naming: Jobs: scheduler.job.task Datasets: instance.schema.table 13

14. 14 Protocol: - Asynchronous events: Unique run id for identifying a run and correlate events - Configurable backend: - Kafka - Http Examples: ● Run Start event ○ source code version ○ run parameters ● Run Complete event ○ input dataset ○ output dataset version and schema

15. 15 Facets ● Extensible: Facets are atomic pieces of metadata identified by a unique name that can be attached to the core entities. ● Decentralized: Prefixes in facet names allow the definition of Custom facets that can be promoted to the spec at a later point.

16. Facet examples Dataset: - Stats - Schema - Version - Column level lineage Job: - Source code - Dependencies - params - Source control - Query plan - Query profile Run: - Schedule time - Batch id

17. 17

18. Metadata: Ingest Storage Compute Streaming Batch/ML ● Data Platform built around Marquez ● Integrations ○ Ingest ○ Storage ○ Compute Flink Airflow Kafka Iceberg / S3 BI OpenLineage

19. Marquez: Data model Job Dataset Job Version Run * 1 * 1 * 1 1 * 1 * Source 1 * ● MYSQL ● POSTGRESQL ● REDSHIFT ● SNOWFLAKE ● KAFKA ● S3 ● ICEBERG ● DELTALAKE ● BATCH ● STREAM ● SERVICE Dataset Version

20. API ● Open Lineage and Marquez standardize metadata collection ○ Job runs ○ Parameters ○ Version ○ Inputs / outputs ● Datakin enables ○ Understanding operational dependencies ○ Impact analysis ○ Troubleshooting: What has changed since the last time it worked? Datakin leverages Marquez metadata Lineage analysis Graph Integrations

21. Spark observability with OpenLineage 21

22. 22 Spark java agent spark.driver.extraJavaOptions: -javaagent:marquez-spark-0.13.1.jar={argument}

23. Metadata collected 23 Lineage: inputs/outputs Data volume: row count/byte size Logical plan

24. Lineage model 24

25. Lineage Example across jobs

26. 26 Example of OpenLineage metadata usage: Data volume evolution

27. Join the conversation OpenLineage: Github: github.com/OpenLineage Slack: OpenLineage.slack.com Twitter: @OpenLineage Email: groups.google.com/g/openlineage Marquez: Github: github.com/MarquezProject/marquez Slack: MarquezProject.slack.com Twitter: @MarquezProject

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