Speeding Time to Insight with a Modern ELT Approach

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The availability of new tools in the modern data stack is changing the way data teams operate. Specifically, the modern data stack supports an “ELT” approach for managing data, rather than the traditional “ETL” approach. In an ELT approach, data sources are automatically loaded in a normalized state into Delta Lake and opinionated transformations happen in the data destination using dbt. This workflow allows data analysts to move more quickly from raw data to insight, while creating repeatable data pipelines robust to changes in the source datasets. In this presentation, we’ll illustrate how easy it is for even a data analytics team of one to to develop an end-to-end data pipeline. We’ll load data from GitHub into Delta Lake, then use pre-built dbt models to feed a daily Redash dashboard on sales performance by manager, and use the same transformed models to power the data science team’s predictions of future sales by segment.

Speeding Time to Insight with a
Modern ELT Approach:
Fivetran + Databricks SQL Service + dbt

Introductions
Sean Spediacci
Fivetran, Sr. Product Marketer
Amy Deora
Fishtown Analytics (keepers of dbt), Head of
Alliances

In 2010, the focus of many analytics teams was on
infrastructure, compute and storage for large data sets
● “How do we build / scale our ETL
infrastructure?”
● “How will we control storage
costs?”
● “How will we design a data
warehouse that is performant?”
“Big Data”

The landscape has shifted
● Constraining compute (On-
premises)
● Perpetual licensing
● ETL outside the database
● Separation of storage +
compute (on-premise)
● Subscription licensing
● Complex pipelines required
for large datasets
Single Node Databases Scalable Databases
● Auto scale cloud DBMS
● Usage based licensing
● Separation of storage and
compute allows for agile
data pipelines
Modern Cloud
Destinations
Pipeline architecture is adapting to the modern cloud technology.

ETL ELT
Sources
Transformation (Modeling)
Process
Raw DataExtraction Process Clean, Usable Datasets
Moving Data Into the Warehouse (EL) is a
highly automatable process.
Data Transformation is different for
every company - it cannot be fully
automated.

Moving the data transformation step to the
warehouse has another benefit - democratizing
access to data.
The data transformation step is now more accessible to more
members of the data team - from BI developers to data
scientists.
No more waiting for an ETL process that happens out of sight.

➔Automatic Data
Updates (DML)
➔Automatic Schema
Migrations (DDL)
➔Automated Recovery
from Failure
(Idempotent)
➔Micro-batched
architecture
➔Extensible Cloud
Functions
About Fivetran

About dbt
dbt is an open source transform tool that allows
anyone comfortable with SQL to author their own data
pipelines

● Users write SQL with “super powers” from python (e.g., loops, macros,
local variables).
● Wraps the right DDL, DML around your SQL to materialize data
models in any warehouse.
● Infers the data lineage graph (DAG) as you code.
● Supports multiple environments, and git-based version control.
● Integrates testing into your pipeline.
● Automates documentation
dbt allows data engineers/analysts to work like
software developers

The modern data stack - with Fivetran, Delta Lake,
Databricks SQL Service and dbt
● Fivetran automates the integration with operational
systems with zero configuration required
● dbt provides a flexible data modeling environment with
best practices from DevOps.
● Connecting directly to the new databricks SQL service
makes building your pipeline fast and easy.

1. Ingest data via a Fivetran automatic connector.
2. Connect dbt to start transforming your data in-
warehouse.
3. Use a a Fivetran dbt package to jump-start your
modeling process.
Speeding time to insight with the modern stack

Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.

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Machine Learning development involves comparing models and storing the artifacts they produced. We often compare several algorithms to select the most efficient ones. We assess different hyper-parameters to fine-tune the model. Git helps us store multiple versions of our code. Additionally, we need to keep track of the datasets we are using. This is important not only for audit purposes but also for assessing the performances of the models, developed at a later time. Git is a standard code versioning tool in software development. It can be used to store your datasets but it does not offer an optimal solution.

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Watch full webinar here: https://bit.ly/3cUA0Qi Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it. Attend this session to learn: - The challenges organizations face when trying to get data to the business users in a timely manner - How Data Virtualization can accelerate time-to-value for an organization’s data assets - Examples of leading companies that used data virtualization to get the right data to the users at the right time

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Data Lakehouse Symposium | Day 1 | Part 1

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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

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Data Lakehouse Symposium | Day 4

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The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.

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

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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

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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

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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

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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

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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 your Data Pipelines with Apache Spark on Kubernetes

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

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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.

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一比一原版(heriotwatt学位证书)英国赫瑞瓦特大学毕业证如何办理

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Speeding Time to Insight with a Modern ELT Approach

1. Speeding Time to Insight with a Modern ELT Approach: Fivetran + Databricks SQL Service + dbt

2. Introductions Sean Spediacci Fivetran, Sr. Product Marketer Amy Deora Fishtown Analytics (keepers of dbt), Head of Alliances

3. In 2010, the focus of many analytics teams was on infrastructure, compute and storage for large data sets ● “How do we build / scale our ETL infrastructure?” ● “How will we control storage costs?” ● “How will we design a data warehouse that is performant?” “Big Data”

4. The landscape has shifted ● Constraining compute (On- premises) ● Perpetual licensing ● ETL outside the database ● Separation of storage + compute (on-premise) ● Subscription licensing ● Complex pipelines required for large datasets Single Node Databases Scalable Databases ● Auto scale cloud DBMS ● Usage based licensing ● Separation of storage and compute allows for agile data pipelines Modern Cloud Destinations Pipeline architecture is adapting to the modern cloud technology.

5. ETL ELT Sources Transformation (Modeling) Process Raw DataExtraction Process Clean, Usable Datasets Moving Data Into the Warehouse (EL) is a highly automatable process. Data Transformation is different for every company - it cannot be fully automated.

6. The Modern Data Stack

7. Moving the data transformation step to the warehouse has another benefit - democratizing access to data. The data transformation step is now more accessible to more members of the data team - from BI developers to data scientists. No more waiting for an ETL process that happens out of sight.

8. ➔Automatic Data Updates (DML) ➔Automatic Schema Migrations (DDL) ➔Automated Recovery from Failure (Idempotent) ➔Micro-batched architecture ➔Extensible Cloud Functions About Fivetran

9. About dbt dbt is an open source transform tool that allows anyone comfortable with SQL to author their own data pipelines

10. ● Users write SQL with “super powers” from python (e.g., loops, macros, local variables). ● Wraps the right DDL, DML around your SQL to materialize data models in any warehouse. ● Infers the data lineage graph (DAG) as you code. ● Supports multiple environments, and git-based version control. ● Integrates testing into your pipeline. ● Automates documentation dbt allows data engineers/analysts to work like software developers

11. The modern data stack - with Fivetran, Delta Lake, Databricks SQL Service and dbt ● Fivetran automates the integration with operational systems with zero configuration required ● dbt provides a flexible data modeling environment with best practices from DevOps. ● Connecting directly to the new databricks SQL service makes building your pipeline fast and easy.

12. 1. Ingest data via a Fivetran automatic connector. 2. Connect dbt to start transforming your data in- warehouse. 3. Use a a Fivetran dbt package to jump-start your modeling process. Speeding time to insight with the modern stack

13. Demo: Instant Insight from github data

14. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

15. Color Palette Primary Colors

16. Bringing it all together

17. Basic Slide ▪ Bullet 1 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 2 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 3 ▪ Sub-bullet ▪ Sub-bullet

18. Reduce Long Titles ▪ Bullet 1 ▪ Sub-bullet ▪ Sub-bullet ▪ Bullet 2 ▪ Sub-bullet ▪ Sub-bullet By splitting them into a short title, and a more detailed subtitle using this slide format that includes a subtitle area

19. Code example

20. Two Columns ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format Headline FormatHeadline Format

21. Attribution Format Second line of attribution This is a template for a quote slide. This is where the quote goes. Attribute the source below…

22. Databricks simplifies data and AI so data teams can innovate faster

23. Logos

24. Databricks Logos

25. Open Source Logos

Speeding Time to Insight with a Modern ELT Approach

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