Apache Kudu: Technical Deep Dive



The document discusses the Spark Operator, which allows deploying, managing, and monitoring Spark clusters on Kubernetes. It describes how the operator extends Kubernetes by defining custom resources and reacting to events from those resources, such as SparkCluster, SparkApplication, and SparkHistoryServer. The operator takes care of common tasks to simplify running Spark on Kubernetes and hides the complexity through an abstract operator library.

REX How Jobteaser got rid of an old data dump job using change data capture (CDC) with Debezium and Kafka.

Flink Forward San Francisco 2022. With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi. by Ethan Guo & Kyle Weller

This document provides a summary of improvements made to Hive's performance through the use of Apache Tez and other optimizations. Some key points include: - Hive was improved to use Apache Tez as its execution engine instead of MapReduce, reducing latency for interactive queries and improving throughput for batch queries. - Statistics collection was optimized to gather column-level statistics from ORC file footers, speeding up statistics gathering. - The cost-based optimizer Optiq was added to Hive, allowing it to choose better execution plans. - Vectorized query processing, broadcast joins, dynamic partitioning, and other optimizations improved individual query performance by over 100x in some cases.

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.

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.

Did you like it? Check out our E-book: Apache NiFi - A Complete Guide https://ebook.getindata.com/apache-nifi-complete-guide Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers. Author: Albert Lewandowski Linkedin: https://www.linkedin.com/in/albert-lewandowski/ ___ Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets. Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries. https://getindata.com​

If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.

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

Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features. We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.

Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016. http://www.meetup.com/SF-Data-Engineering/events/228293610/ Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases? What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture. This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.

When interacting with analytics dashboards in order to achieve a smooth user experience, two major key requirements are sub-second response time and data freshness. Cluster computing frameworks such as Hadoop or Hive/Hbase work well for storing large volumes of data, although they are not optimized for ingesting streaming data and making it available for queries in realtime. Also, long query latencies make these systems sub-optimal choices for powering interactive dashboards and BI use-cases. In this talk we will present Druid as a complementary solution to existing hadoop based technologies. Druid is an open-source analytics data store, designed from scratch, for OLAP and business intelligence queries over massive data streams. It provides low latency realtime data ingestion and fast sub-second adhoc flexible data exploration queries. Many large companies are switching to Druid for analytics, and we will cover how druid is able to handle massive data streams and why it is a good fit for BI use cases. Agenda - 1) Introduction and Ideal Use cases for Druid 2) Data Architecture 3) Streaming Ingestion with Kafka 4) Demo using Druid, Kafka and Superset. 5) Recent Improvements in Druid moving from lambda architecture to Exactly once Ingestion 6) Future Work

Presto is a distributed SQL query engine that was developed by Facebook to make SQL queries scalable for large datasets. It translates SQL queries into multiple parallel tasks that can process data across many servers without using intermediate storage. This allows Presto to handle millions of records per second. Presto is now open source and used by many companies for interactive analysis of petabyte-scale datasets.

Learn about Hudi's architecture, concurrency control mechanisms, table services and tools. By : Abhishek Modi, Balajee Nagasubramaniam, Prashant Wason, Satish Kotha, Nishith Agarwal

Data in Hadoop is getting bigger every day, consumers of the data are growing, organizations are now looking at making their Hadoop cluster compliant to federal regulations and commercial demands. Apache Ranger simplifies the management of security policies across all components in Hadoop. Ranger provides granular access controls to data. The deck describes what security tools are available in Hadoop and their purpose then it moves on to discuss in detail Apache Ranger.

Raft protocol has been successfully used for consistent metadata replication; however, using it for data replication poses unique challenges. Apache Ratis is a RAFT implementation targeted at high throughput data replication problems. Apache Ratis is being successfully used as a consensus protocol for data stored in Ozone (object store) and Quadra(block device) to provide data throughput that saturates the network links and disk bandwidths. Pluggable nature of Ratis renders it useful for multiple use cases including high availability, data or metadata replication, and ensuring consistency semantics. This talk presents the design challenges to achieve high throughput and how Apache Ratis addresses them. We talk about specific optimizations that have been implemented to minimize overheads and scale up the throughput while maintaining correctness of the consistency protocol. The talk also explains how systems like Ozone take advantage of Ratis’s implementation choices to achieve scale. We will discuss the current performance numbers and also future optimizations. MUKUL KUMAR SINGH, Staff Software Engineer, Hortonworks and LOKESH JAIN, Software Engineer, Hortonworks

OSA Con 2022: Apache Iceberg: An Architectural Look Under the Covers Alex Merced - Dremio The data lakehouse is one of the most exciting trends in the data space promising to merge the best aspects of data lakes and data warehouses without either of their problems. Open source tech is making this promise a reality and in this talk Dremio Developer Advocate, Alex Merced, explores these technologies. In this talk Alex Merced will cover: - What is a Data Lakehouse? - Why open matters in preserving the promise of lakehouses (better costs, vendor freedom, data freedom) - What are technologies that enable lakehouses like Apache Iceberg, Apache Parquet, Apache Arrow and Project Nessie

For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.

3 Things to Learn About:
 *How Apache Kudu enables users to do more than ever before with their Analytic and Operational Databases *How Cloudera has built two versatile databases to help our customers tackle their hardest problems. *How the addition of Apache Kudu to this mix will enable new use cases around real-time analytics, internet of things, time series data, and more.

Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.

The document discusses how Sparklyr allows data scientists to access and work with data stored in Cloudera Enterprise using the popular RStudio IDE. It describes the challenges data scientists face in accessing secured Hadoop clusters and limitations of notebook environments. Sparklyr integration with RStudio provides a familiar environment for data scientists to access Hadoop data and compute using Spark, enabling distributed data science workflows directly in R. The presentation demonstrates how to analyze over a billion records using Spark and R through Sparklyr.

3 Things to Learn About: * On-premises versus the cloud: What’s the same and what’s different?
 * Design and benefits of analytics in the cloud
 * Best practices and architectural considerations

3 Things to Learn About: *The IoT ecosystem and data management considerations for IoT *Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data *Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data


3 Things to Learn About:

 *On-premises versus the cloud: What’s the same and what’s different? *Benefits of data processing in the cloud *Best practices and architectural considerations

Kudu is an open source storage layer developed by Cloudera that provides low latency queries on large datasets. It uses a columnar storage format for fast scans and an embedded B-tree index for fast random access. Kudu tables are partitioned into tablets that are distributed and replicated across a cluster. The Raft consensus algorithm ensures consistency during replication. Kudu is suitable for applications requiring real-time analytics on streaming data and time-series queries across large datasets.

This document provides an introduction to Apache Kudu, a storage layer for Apache Hadoop designed for fast analytics on fast data. It discusses Kudu's motivations of filling gaps in HDFS and HBase capabilities, its design goals of high throughput scans and low latency reads/writes, and how its columnar storage and integration with tools like Spark and Impala enable it to meet these goals. Example use cases like time series and real-time analytics are presented. The document also covers Kudu's architecture of tables and tablets, its replication and fault tolerance model using Raft consensus, and performance comparisons that show it outperforming other storage systems.

The Kite SDK is an open source set of libraries, tools, examples, and documentation focused on helping developers build systems on top of the Apache Hadoop ecosystem. Learn (via examples) how Kite makes it easier to work with data in HDFS and Apache HBase as records and datasets, just as you would with a relational database.

Enterprise workflows in Hadoop using Oozie @ Riot Games. Simple use cases and lessons learned from our platform growth.

社内のHadoop本読書会のスライドです

This document discusses Oozie's high availability (HA) solution. It allows multiple Oozie servers to run against the same database, providing active-active HA and horizontal scalability. A load balancer provides a single entry point to the Oozie servers. ZooKeeper coordinates the servers and enables services like locking and log streaming across servers. Configuration changes allow point Oozie to the ZooKeeper ensemble and load balancer, and enable HA services. Users can now view and access logs from any server, without needing to know which server processed their jobs.

Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.

Data ingest is a deceptively hard problem. In the world of big data processing, it becomes exponentially more difficult. It's not sufficient to simply land data on a system, that data must be ready for processing and analysis. The Kite SDK is a data API designed for solving the issues related to data infest and preparation. In this talk you'll see how Kite can be used for everything from simple tasks to production ready data pipelines in minutes.

Organizations have a wealth of data contained within the existing infrastructures. At DellEMC we’re helping customers remove the barriers of legacy datastores and transforming the customer experience in the modern datacentre. Learn how to unshackle the valuable data inside your existing data warehouse, leverage new techniques, applications and technology to enhance the financial impact of all your data sources

Cloud environments are increasingly becoming a popular deployment option for Hadoop. Enterprises can take advantage of the added flexibility and elasticity of the cloud for both long-running clusters, temporary deployments or for spikey workloads. However, as more and more users choose cloud environments for critical Hadoop workloads, they are often forced to compromise on key aspects of their data platform. Cloudera Director enables the full fidelity of the Enterprise Data Hub in the cloud, without compromises. Announced with the recent 5.2 release, Cloudera Director is the simple, reliable way to deploy and scale Hadoop in the cloud, while maintaining an open and neutral platform with enterprise-grade capabilities. During this webinar, Tushar Shanbhag, Director of Product Management, will look at why Hadoop cloud environments are becoming so popular and some of the challenges around Hadoop in the cloud. He will then provide an in-depth overview of Cloudera Director, its key features, and how it alleviates these common challenges. Finally, he will discuss some key use cases and provide insight into what’s next for Cloudera and Hadoop in the cloud.

Apache Oozie has come a long way and now accounts for over 2.8 Million jobs per month on Yahoo's grid infrastructure. If you are running Hadoop jobs repeatedly and thinking of a smarter way of doing it, Apache Oozie is the answer. Be it running complex data transformation jobs chained one after another or simple daily data copy, Oozie workflows will help you to manage these tasks efficiently. Mona will cover the new features introduced in Apache Oozie 4.x, in particular, Apache HCatalog Integration, Job Notifications and SLA Monitoring for building large-scale and efficient data processing pipelines.

Your data is your IP and its security is paramount. The last thing you want is for your data to become a target for threats. This workshop will focus on the realities of protecting your customer’s IP from external and internal threats with battle hardened technologies and methodologies. Another key concept that will be examined is the connection of people, processes and technology. In addition, the session will take a look at authentication and authorisation, auditing and data lineage as well as the different groups required to play a part in the modern data hub. We will also look at how to produce high impact operation reports from Cloudera’s RecordService a new core security layer that centrally enforces fine-grained access control policy, which helps close the feedback loop to ensure awareness of security as a living entity within your organisation.