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As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends. * Introduction to Data Engineering * Role of Big Data in Data Engineering * Key Skills related to Data Engineering * Role of Big Data in Data Engineering * Overview of Data Engineering Certifications * Free Content and ITVersity Paid Resources Don't worry if you miss the video - you can click on the below link to go through the video after the schedule. https://youtu.be/dj565kgP1Ss * Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/ Relevant Playlists: * Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi * Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl * Join our Meetup group - https://www.meetup.com/itversityin/ * Enroll for our labs - https://labs.itversity.com/plans * Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1 * Access Content via our GitHub - https://github.com/dgadiraju/itversity-books * Lab and Content Support using Slack

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Netflix’s Big Data Platform team manages data warehouse in Amazon S3 with over 60 petabytes of data and writes hundreds of terabytes of data every day. With a data warehouse at this scale, it is a constant challenge to keep improving performance. This talk will focus on Iceberg, a new table metadata format that is designed for managing huge tables backed by S3 storage. Iceberg decreases job planning time from minutes to under a second, while also isolating reads from writes to guarantee jobs always use consistent table snapshots. In this session, you'll learn: • Some background about big data at Netflix • Why Iceberg is needed and the drawbacks of the current tables used by Spark and Hive • How Iceberg maintains table metadata to make queries fast and reliable • The benefits of Iceberg's design and how it is changing the way Netflix manages its data warehouse • How you can get started using Iceberg Speaker Ryan Blue, Software Engineer, Netflix

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Thoughtworks Zhamak Dehghani observations on these traditional approaches’s failure modes, inspired her to develop an alternative big data management architecture that she aptly named the Data Mesh. This represents a paradigm shift that draws from modern distributed architecture and is founded on the principles of domain-driven design, self-serve platform, and product thinking with Data. In the last decade Apache Kafka has established a new category of data management infrastructure for data in motion that has been leveraged in modern distributed data architectures.

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In this talk we will present how Databricks has enabled the author to achieve more with data, enabling one person to build a coherent data project with data engineering, analysis and science components, with better collaboration, better productionalization methods, with larger datasets and faster. The talk will include a demo that will illustrate how the multiple functionalities of Databricks help to build a coherent data project with Databricks jobs, Delta Lake and auto-loader for data engineering, SQL Analytics for Data Analysis, Spark ML and MLFlow for data science, and Projects for collaboration.

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With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!

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A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.

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Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems. There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.

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Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.

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Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.

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At WeWork, it's critical that we understand the complete context for all datasets. We also want to be able to explore dependencies between jobs and the datasets they produce and consume. To do this, WeWork needs metadata. In this talk I will focus on Marquez, a core service for the collection, aggregation and visualization of a data ecosystems metadata. Marquez maintains the provenance of how datasets are consumed and produced while providing global visibility into job runtime.

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Data Mesh

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Data Lakes have been built with a desire to democratize data - to allow more and more people, tools, and applications to make use of data. A key capability needed to achieve it is hiding the complexity of underlying data structures and physical data storage from users. The de-facto standard has been the Hive table format addresses some of these problems but falls short at data, user, and application scale. So what is the answer? Apache Iceberg. Apache Iceberg table format is now in use and contributed to by many leading tech companies like Netflix, Apple, Airbnb, LinkedIn, Dremio, Expedia, and AWS. Watch Alex Merced, Developer Advocate at Dremio, as he describes the open architecture and performance-oriented capabilities of Apache Iceberg. You will learn: • The issues that arise when using the Hive table format at scale, and why we need a new table format • How a straightforward, elegant change in table format structure has enormous positive effects • The underlying architecture of an Apache Iceberg table, how a query against an Iceberg table works, and how the table’s underlying structure changes as CRUD operations are done on it • The resulting benefits of this architectural design

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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.

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Apache Spark has been gaining steam, with rapidity, both in the headlines and in real-world adoption. Spark was developed in 2009, and open sourced in 2010. Since then, it has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. This open source analytics engine stands out for its ability to process large volumes of data significantly faster than contemporaries such as MapReduce, primarily owing to in-memory storage of data on its own processing framework. That being said, one of the top real-world industry use cases for Apache Spark is its ability to process ‘streaming data‘.

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Deploying machine learning models seems like it should be a relatively easy task. Take your model and pass it some features in production. The reality is that the code written during the prototyping phase of model development doesn’t always work when applied at scale or on “real” data. This talk will explore 1) common problems at the intersection of data science and data engineering 2) how you can structure your code so there is minimal friction between prototyping and production, and 3) how you can use Apache Spark to run predictions on your models in batch or streaming contexts. You will take away how to address some of productionizing issues that data scientists and data engineers face while deploying machine learning models at scale and a better understanding of how to work collaboratively to minimize disparity between prototyping and productizing.

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Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. This talk offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way. Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that make it easier to get started with Spark and transition themselves to a daily workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix. In this talk, we look at Stitch Fix’s journey, exploring its Spark setup, in-house tools and how they work in synergy with open source frameworks in a cloud environment. There are additional improvements to the infrastructure that help persist information for future use and optimization and we look at how the implementation of Amazon’s EMR FS has helped make it easier for us to read from the S3 source.

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Parquet is a columnar format designed to be extremely efficient and interoperable across the hadoop ecosystem. Its integration in most of the Hadoop processing frameworks (Impala, Hive, Pig, Cascading, Crunch, Scalding, Spark, …) and serialization models (Thrift, Avro, Protocol Buffers, …) makes it easy to use in existing ETL and processing pipelines, while giving flexibility of choice on the query engine (whether in Java or C++). In this talk, we will describe how one can us Parquet with a wide variety of data analysis tools like Spark, Impala, Pig, Hive, and Cascading to create powerful, efficient data analysis pipelines. Data management is simplified as the format is self describing and handles schema evolution. Support for nested structures enables more natural modeling of data for Hadoop compared to flat representations that create the need for often costly joins.

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