Hudi architecture, fundamentals and capabilities

An Open Source Incremental Processing Framework called Hoodie is summarized. Key points: - Hoodie provides upsert and incremental processing capabilities on top of a Hadoop data lake to enable near real-time queries while avoiding costly full scans. - It introduces primitives like upsert and incremental pull to apply mutations and consume only changed data. - Hoodie stores data on HDFS and provides different views like read optimized, real-time, and log views to balance query performance and data latency for analytical workloads. - The framework is open source and built on Spark, providing horizontal scalability and leveraging existing Hadoop SQL query engines like Hive and Presto.

Hyperspace is a recently open-sourced (https://github.com/microsoft/hyperspace) indexing sub-system from Microsoft. The key idea behind Hyperspace is simple: Users specify the indexes they want to build. Hyperspace builds these indexes using Apache Spark, and maintains metadata in its write-ahead log that is stored in the data lake. At runtime, Hyperspace automatically selects the best index to use for a given query without requiring users to rewrite their queries. Since Hyperspace was introduced, one of the most popular asks from the Spark community was indexing support for Delta Lake. In this talk, we present our experiences in designing and implementing Hyperspace support for Delta Lake and how it can be used for accelerating queries over Delta tables. We will cover the necessary foundations behind Delta Lake’s transaction log design and how Hyperspace enables indexing support that seamlessly works with the former’s time travel queries.

Hive tables are an integral part of the big data ecosystem, but the simple directory-based design that made them ubiquitous is increasingly problematic. Netflix uses tables backed by S3 that, like other object stores, don’t fit this directory-based model: listings are much slower, renames are not atomic, and results are eventually consistent. Even tables in HDFS are problematic at scale, and reliable query behavior requires readers to acquire locks and wait. Owen O’Malley and Ryan Blue offer an overview of Iceberg, a new open source project that defines a new table layout addresses the challenges of current Hive tables, with properties specifically designed for cloud object stores, such as S3. Iceberg is an Apache-licensed open source project. It specifies the portable table format and standardizes many important features, including: * All reads use snapshot isolation without locking. * No directory listings are required for query planning. * Files can be added, removed, or replaced atomically. * Full schema evolution supports changes in the table over time. * Partitioning evolution enables changes to the physical layout without breaking existing queries. * Data files are stored as Avro, ORC, or Parquet. * Support for Spark, Pig, and Presto.

Optimizing spark jobs through a true understanding of spark core. Learn: What is a partition? What is the difference between read/shuffle/write partitions? How to increase parallelism and decrease output files? Where does shuffle data go between stages? What is the "right" size for your spark partitions and files? Why does a job slow down with only a few tasks left and never finish? Why doesn't adding nodes decrease my compute time?

As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements. 1) Generality: support reading/writing most data management/storage systems. 2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities. Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.

Data Orchestration Summit www.alluxio.io/data-orchestration-summit-2019 November 7, 2019 Apache Iceberg - A Table Format for Hige Analytic Datasets Speaker: Ryan Blue, Netflix For more Alluxio events: https://www.alluxio.io/events/

The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads. As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general. This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.

Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.

The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.

We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.

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

Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner. In this talk, I am going examine a number common streaming design patterns in the context of the following questions. WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements? WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements? HOW are going to architect the solution? And how much are you willing to pay for it? Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.

Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.

This document describes the Delta architecture, which unifies batch and streaming data processing. Delta achieves this through a continuous data flow model using structured streaming. It allows data engineers to read consistent data while being written, incrementally read large tables at scale, rollback in case of errors, replay and process historical data along with new data, and handle late arriving data without delays. Delta uses transaction logging, optimistic concurrency, and Spark to scale metadata handling for large tables. This provides a simplified solution to common challenges data engineers face.

Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with

This document discusses 5 common mistakes when writing Spark applications: 1) Improperly sizing executors by not considering cores, memory, and overhead. The optimal configuration depends on the workload and cluster resources. 2) Applications failing due to shuffle blocks exceeding 2GB size limit. Increasing the number of partitions helps address this. 3) Jobs running slowly due to data skew in joins and shuffles. Techniques like salting keys can help address skew. 4) Not properly managing the DAG to avoid shuffles and bring work to the data. Using ReduceByKey over GroupByKey and TreeReduce over Reduce when possible. 5) Classpath conflicts arising from mismatched library versions, which can be addressed using sh

What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet. At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it. We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).

This document provides an overview and introduction to BigData using Hadoop and Pig. It begins with introducing the speaker and their background working with large datasets. It then outlines what will be covered, including an introduction to BigData, Hadoop, Pig, HBase and Hive. Definitions and examples are provided for each. The remainder of the document demonstrates Hadoop and Pig concepts and commands through code examples and explanations.

Presented at Open Source India 2016, at a workshop titled: Building a Data Lake using Apache Hadoop: A Proof of Concept

This document discusses analytics in the cloud using Amazon Elastic MapReduce (EMR). It describes how big data is growing rapidly in size and complexity, making it difficult for traditional systems to handle. Hadoop and MapReduce provide scalable and cost-effective solutions for big data analytics. Amazon EMR makes it easy to set up and manage Hadoop clusters in the AWS cloud, reducing costs and complexity compared to operating Hadoop on-premises. It also integrates tightly with other AWS services like S3 and EC2.

Michael Sun presented on CBS Interactive's use of Hadoop for web analytics processing. Some key points: - CBS Interactive processes over 1 billion web logs daily from hundreds of websites on a Hadoop cluster with over 1PB of storage. - They developed an ETL framework called Lumberjack in Python for extracting, transforming, and loading data from web logs into Hadoop and databases. - Lumberjack uses streaming, filters, and schemas to parse, clean, lookup dimensions, and sessionize web logs before loading into a data warehouse for reporting and analytics. - Migrating to Hadoop provided significant benefits including reduced processing time, fault tolerance, scalability, and cost effectiveness compared to their

The document provides an overview of big data, analytics, Hadoop, and related concepts. It discusses what big data is and the challenges it poses. It then describes Hadoop as an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop introduced include HDFS for storage, MapReduce for parallel processing, and various other tools. A word count example demonstrates how MapReduce works. Common use cases and companies using Hadoop are also listed.

This document discusses optimizations made to Alibaba Cloud's Data Lake Analytics (DLA) engine, which uses Presto, to improve performance when querying data stored in Object Storage Service (OSS). The optimizations included decreasing OSS API request counts, implementing an Alluxio data cache using local disks on Presto workers, and improving disk throughput by utilizing multiple ultra disks. These changes increased cache hit ratios and query performance for workloads involving large scans of data stored in OSS. Future plans include supporting an Alluxio cluster shared by multiple users and additional caching techniques.

1. The document discusses using Hadoop and Hive at Zing to build a log collecting, analyzing, and reporting system. 2. Scribe is used for fast log collection and storing data in Hadoop/Hive. Hive provides SQL-like queries to analyze large datasets. 3. The system transforms logs into Hive tables, runs analysis jobs in Hive, then exports data to MySQL for web reporting. This provides a scalable, high performance solution compared to the initial RDBMS-only system.

Qubole is a cloud-based platform that allows customers to easily run Hadoop and Spark clusters on AWS for big data analytics. It optimizes performance and reduces costs through techniques like caching data in S3 for faster access, using spot instances, and directly writing query outputs to S3. The document discusses Qubole's features, capabilities, and how it provides an easier way for more users like data scientists and analysts to access and query big data compared to building and managing Hadoop clusters themselves.

The document discusses using Hadoop and Hive at Zing for log collecting, analyzing, and reporting. It provides an overview of Hadoop and Hive and how they are used at Zing to store and analyze large amounts of log and user data in a scalable, fault-tolerant manner. A case study is presented that describes how Zing evolved its log analysis system from using MySQL to using Scribe, Hadoop, and Hive to more efficiently collect, transform, analyze and report on log data.

Hypertable Distilled document report as pdf. Underway as another format for presentation. Thanks for your patient!

This document provides an overview of a data analytics session covering big data architecture, connecting and extracting data from storage, traditional processing with a bank use case, Hadoop-HDFS solutions, and HDFS working. The key topics covered include big data architecture layers, structured and unstructured data extraction, comparisons of storage media, traditional versus Hadoop approaches, HDFS basics including blocks and replication across nodes. The session aims to help learners understand efficient analytics systems for handling large and diverse data sources.

Strata Data Conference, London, May 2017 Trent Gray-Donald and Gil Vernik explain the challenges of current Hadoop and Apache Spark integration with object stores and discuss Stocator, an open source (Apache License 2.0) object store connector for Hadoop and Apache Spark specifically designed to optimize their performance with object stores. Trent and Gil describe how Stocator works and share real-life examples and benchmarks that demonstrate how it can greatly improve performance and reduce the quantity of resources used.

This document discusses using object stores for unstructured data storage instead of HDFS. It summarizes that existing Hadoop components are inefficient for working with object stores due to algorithms that create temporary files and supporting full HDFS shell functionality. An alternative approach called Stocator is presented that is optimized for object stores by directly interacting with them without Hadoop modules. Stocator provides significant performance gains over HDFS connectors by generating fewer requests and being tailored for analytic workflows rather than full HDFS compatibility. Case studies with SETI show Stocator processing large datasets from object stores much faster than HDFS connectors.

Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.

The document discusses Project Voldemort, a distributed key-value storage system developed at LinkedIn. It provides an overview of Voldemort's motivation and features, including high availability, horizontal scalability, and consistency guarantees. It also describes LinkedIn's use of Voldemort and Hadoop for applications like event logging, online lookups, and batch processing of large datasets.

The document provides an overview of big data analytics and Hadoop. It defines big data and the challenges of working with large, complex datasets. It then discusses Hadoop as an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. Key components of Hadoop include HDFS for storage, MapReduce for parallel processing, and other tools like Pig, Hive, HBase etc. The document provides examples of how Hadoop is used by many large companies and describes the architecture and basic functions of HDFS and MapReduce.

This document provides an overview of big data concepts, including NoSQL databases, batch and real-time data processing frameworks, and analytical querying tools. It discusses scalability challenges with traditional SQL databases and introduces horizontal scaling with NoSQL systems like key-value, document, column, and graph stores. MapReduce and Hadoop are described for batch processing, while Storm is presented for real-time processing. Hive and Pig are summarized as tools for running analytical queries over large datasets.

The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.

Hadoop Summit 2010 - Developers Track Hadoop Frameworks Panel: Pig, Hive, Cascading, Cloudera Desktop, LinkedIn Voldemort, Twitter ElephantBird Moderator: Sanjay Radia, Yahoo!

This document discusses Hadoop and Hive development at Facebook, including how they generate large amounts of user data daily, how they store the data in Hadoop clusters, and how they use Hive as a data warehouse to efficiently run SQL queries on the Hadoop data using a SQL-like language. It also outlines some of Hive's architecture and features like partitioning, buckets, and UDF/UDAF support, as well as its performance improvements over time and future planned work.

이 세션에서는 SageMaker Training Jobs / SageMaker Jumpstart를 사용하여 Foundation Model 을 Pre-Triaining ���거나 Fine Tuing 하는 방안을 제시합니다. 이 세션을 통해 아래 3가지가 소개됩니다. 1. 파운데이션 모델을 처음부터 Training 2. 오픈 소스 모델을 사용하여 파운데이션 모델을 Pre-Training 3. 도메인에 맞게 모델을 Fine Tuning하는 방안 발표자: Miron Perel, Principal ML GTM Specialist, AWS Kristine Pearce, Principal ML BD, AWS

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Democratizing Data – Why Data Mesh ?

Aurora PostgreSQL에서 가장 일반적인 performance use case 들에 대해 Aurora PostreSQL의 모니터링 Tool들을 통해 어떤게 문제를 식별하고 분석하는지 그리고 이 문제를 해결해나가는 절차와 방법에 대한 Deep Dive입니다.

Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance. A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen. Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second. In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.

Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados. QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo. Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.

Airline Satisfaction Project using Azure This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.

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