Sub-Second SQL Search, Aggregations and Joins with Kafka and Rockset | Dhruba Borthakur, Rockset
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We often need to build applications that analyze Kafka data to unlock the most value from event streams, so how can organizations build these real-time analytics applications? In this talk, we examine an indexing approach that enables fast SQL analytics on data from Kafka, without data flattening or denormalization. Rockset is the real-time indexing database that builds an inverted index, a columnar index and a row index on all fields of your Kafka messages, including nested fields and arrays. This Converged Index accelerates various types of analytic queries–search, aggregations and joins–without the need to denormalize or transform data for performance reasons. With indexing delivering significant gains in query performance, we also need to index new data in a timely manner. We discuss several strategies used for efficient ingestion and indexing from Kafka, including rollups, write optimizations on the underlying RocksDB storage engine, and the disaggregation of ingest and query compute.
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Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
From Batch to Streaming with Apache Apex Dataworks Summit 2017
This document discusses transitioning from batch to streaming data processing using Apache Apex. It provides an overview of Apex and how it can be used to build real-time streaming applications. Examples are given of how to build an application that processes Twitter data streams and visualizes results. The document also outlines Apex's capabilities for scalable stream processing, queryable state, and its growing library of connectors and transformations.
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You Must Construct Additional Pipelines: Pub-Sub on Kafka at Blizzard
(Stephen Parente + Jeff Field, Blizzard) Kafka Summit SF 2018
Blizzard’s global data platform has become a driving force in both business and operational analytics. As more internal customers onboard with the system, there is increasing demand for custom applications to access this data in near real time. In order to avoid many independent teams with varying levels of Kafka expertise all accessing the firehose from our critical production Kafkas, we developed our own pub-sub system on top of Kafka to provide specific datasets to customers on their own cloud deployed Kafka clusters.
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
As a 120 year-old company, Nordstrom was facing numerous challenges as a result of an aging, service-oriented, architecture. Developers needing to implement reporting for analytics separately from core functionality resulted in questionable data quality for analytical purposes. Scaling dependent services in harmony to not overwhelm each other was a struggle faced by many, if not most, teams. Several years into a company-wide transition to an event-sourced architecture, Nordstrom has solved these and various other problems. By leveraging the capabilities of Apache Kafka and Confluent, combined with a deep organizational focus on well-defined business event schemas, a singular event can be used for analytical, functional, operational, and model building purposes. This session will describe this architecture and the lessons learned while building it, with a focus on the internally built, multi-tenant, multi-cluster, Kafka-as-a-Service platform that enables it.
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...
Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.
Bringing Streaming Data To The Masses: Lowering The “Cost Of Admission” For Y...
(Bob Lehmann, Bayer) Kafka Summit SF 2018
You’ve built your streaming data platform. The early adopters are “all in” and have developed producers, consumers and stream processing apps for a number of use cases. A large percentage of the enterprise, however, has expressed interest but hasn’t made the leap. Why?
In 2014, Bayer Crop Science (formerly Monsanto) adopted a cloud first strategy and started a multi-year transition to the cloud. A Kafka-based cross-datacenter DataHub was created to facilitate this migration and to drive the shift to real-time stream processing. The DataHub has seen strong enterprise adoption and supports a myriad of use cases. Data is ingested from a wide variety of sources and the data can move effortlessly between an on premise datacenter, AWS and Google Cloud. The DataHub has evolved continuously over time to meet the current and anticipated needs of our internal customers. The “cost of admission” for the platform has been lowered dramatically over time via our DataHub Portal and technologies such as Kafka Connect, Kubernetes and Presto. Most operations are now self-service, onboarding of new data sources is relatively painless and stream processing via KSQL and other technologies is being incorporated into the core DataHub platform.
In this talk, Bob Lehmann will describe the origins and evolution of the Enterprise DataHub with an emphasis on steps that were taken to drive user adoption. Bob will also talk about integrations between the DataHub and other key data platforms at Bayer, lessons learned and the future direction for streaming data and stream processing at Bayer.
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
Apache Druid is a high-performance distributed analytics store for modern analytics applications. It supports ingesting millions of events per second and sub-second query processing. Druid supports various types of data sources for ingestion, including Apache Kafka. You can immediately query on stream events once they get ingested into Druid. Since Kafka provides scalable and robust data delivery while Druid supports advanced complex analysis on streams, Kafka and Druid are widely used together for BI and operational analytics use cases, which require interactivity, scalability, real-time, and performance.
This talk is based on our real-world experiences building out streaming analytics stacks powering production use cases across many industries.
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Today, many companies that have lots of data are still struggling to derive value from machine learning (ML) and data science investments. Why? Accessing the data may be difficult. Or maybe it’s poorly labeled. Or vital context is missing. Or there are questions around data integrity. Or standing up an ML service can be cumbersome and complex.
At Nuuly, we offer an innovative clothing rental subscription model and are continually evolving our ML solutions to gain insight into the behaviors of our unique customer base as well as provide personalized services. In this session, I’ll share how we used event streaming with Apache Kafka® and Confluent Cloud to address many of the challenges that may be keeping your organization from maximizing the business value of machine learning and data science. First, you’ll see how we ensure that every customer interaction and its business context is collected. Next, I’ll explain how we can replay entire interaction histories using Kafka as a transport layer as well as a persistence layer and a business application processing layer. Order management, inventory management, logistics, subscription management – all of it integrates with Kafka as the common backbone. These data streams enable Nuuly to rapidly prototype and deploy dynamic ML models to support various domains, including pricing, recommendations, product similarity, and warehouse optimization. Join us and learn how Kafka can help improve machine learning and data science initiatives that may not be delivered to their full potential.
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
This document discusses streaming data between Confluent Cloud and MongoDB Atlas. It provides an overview of MongoDB Atlas and its fully managed database capabilities in the cloud. It then demonstrates how to stream data from a Python generator application to MongoDB Atlas using Confluent Cloud and its connectors. The presentation concludes by providing a reference architecture for connecting Confluent Platform to MongoDB.
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
Studying the ""how"" of Kafka makes you better at using Kafka, but studying its ""whys"" makes you better at so much more. In looking at the tradeoffs behind a system like Kafka, we learn to reason more clearly about distributed systems and to make high-stakes technology adoption decisions more effectively. These are skills we all want to improve!
In this talk, we'll examine trade-offs on which our favorite distributed messaging system takes opinionated positions:
- Whether to store data contiguously or using an index
- How many storage tiers are best?
- Where should metadata live?
- And more.
It's always useful to dissect a modern distributed system with the goal of understanding it better, and it's even better to learn to deeper architectural principles in the process. Come to this talk for a generous helping of both.
PCAP Graphs for Cybersecurity and System Tuning
This document discusses analyzing network traffic patterns in Hadoop clusters. Packet capture data was collected from example Hadoop workloads and analyzed using Gephi. Initial results show the network structure and communication between nodes for batch processing (TeraSort) and real-time streaming (Twitter collection). Further analysis aims to classify components, understand dependencies, and identify anomalies over time to better understand typical and atypical workload behavior.
How a distributed graph analytics platform uses Apache Kafka for data ingesti...
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. In the TigerGraph database, Kafka Connect framework was used to build the native S3 data loader. In TigerGraph Cloud, we will be building native integration with many data sources such as Azure Blob Storage and Google Cloud Storage using Kafka as an integrated component for the Cloud Portal.
In this session, we will be discussing both architectures: 1. built-in Kafka Connect framework within TigerGraph database; 2. using Kafka cluster for cloud native integration with other popular data sources. Demo will be provided for both data streaming processes.
Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...
This document discusses testing event-driven architectures. It begins by defining common event-driven architecture patterns like event notifications and event sourcing. It then discusses brokering the complexity of event-driven architectures by describing how events are communicated between producers and consumers via channels. The document outlines what information should be included in events like payloads and headers. It also discusses the difference between orchestration and choreography in event-driven systems. It provides an example of how events can be used to mediate changes within a system using order validation. Finally, it demonstrates how to test event-driven architectures using specifications and discusses accelerating API quality through testing tools that support multiple protocols and definitions.
Cloud-Based Event Stream Processing Architectures and Patterns with Apache Ka...
The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.
Enterprise Metadata Integration
EMI supports knowledge management for better Data Science.
Presented @GraphConnect 2017 (QEII Centre London)
Self-service Events & Decentralised Governance with AsyncAPI: A Real World Ex...
Despite great advances in Kafka's SaaS offerings it can still be challenging to create a sustainable event-driven ecosystem. Often platform engineers become de facto ‘gatekeepers’ of events & topics, yet their day job is not about data modelling or domain expertise. We've all seen the bottlenecks these unsustainable processes create.
Realising the potential of event streams requires much more than infrastructure. Beyond an event-driven mindset, it requires domain experts to lead creation of well-defined discoverable events through fit-for-purpose governance. AsyncAPI is the OpenAPI for events that can form the basis of the required self-governing, self-service eventing framework.
This session will introduce a self-governing framework using AsyncAPI and share how the Bank of New Zealand applied this framework to leverage a passionate Kafka community and embed event-driven thinking. You’ll leave with a tangible set of ideas to give your own events a bit more swagger using AsyncAPI.
Simplifying Event Streaming: Tools for Location Transparency and Data Evoluti...
At Under Armour Connected Fitness, we’ve built an event streaming platform on top of Kafka and the Confluent stack that makes it easy for developers to produce and consume schema-based events without requiring direct knowledge of Kafka. We are constantly trying to improve the developer experience. The platform consists of multiple federated Kafka clusters, a schema registry, a topology service, an archiver and specialized client libraries and Web / CLI tools that assist developers with producer and consumer workflows.
In this talk, we will take a deeper dive into the design and implementation of a Scala/Java implementation of our client library that allows developers to produce or consume events without worrying about the underlying infrastructure and their location while enjoying the benefits of data compatibility through schemas. We’ll also look at an HTTP based client proxy that exposes the same API but for languages without our native support. Finally, we’ll walk through Web and CLI tools we built to make working with the platform easier.
The content of this talk will be primarily aimed at software developers looking for ideas on how to build Kafka client tools that allow producer/consumer interactions protected by schema-based event definitions while hiding details of the underlying infrastructure.
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
In this talk, Confluent co-founder and CEO, Jay Kreps will cover the rise of two trends:
1. The rise of Apache Kafka and event streams
2. The rise of the public cloud and cloud-native data systems
... and the problems we need to solve as these two trends come together.
Death of the dumb pipes: Using Apache Kafka® for Integration projects
Guru Sattanathan, Confluent, Senior Solutions Engineer
Enterprise Integration technologies (aka Middleware) are the key enablers when it comes to Real-time data flows or Event Driven Architecture. Starting from real-time payments, e-commerce, travel booking systems, etc, everything is powered by a middleware underneath. It did transform a lot of things but with caveats!
Are ESB’s & MQ’s enough for today’s integration needs? Do you know their technical debts?
If you are someone looking at integrating your applications or an Integration Architect this session is for you. It's time to refresh yourself and see how organizations are building integrations today.
In this session, we will go in this order:
-Recap on Enterprise Integration technologies
-What are the key flaws & What needs improvement?
-What is Apache Kafka?
-Rethinking Integration using Apache Kafka
https://www.meetup.com/KafkaMelbourne/events/280590162/
SIEM Modernization: Build a Situationally Aware Organization with Apache Kafka®
Watch this talk here: https://www.confluent.io/online-talks/siem-modernization-build-a-situationally-aware-organization-with-apache-kafka
Of all security breaches, 85% are conducted with compromised credentials, often at the administration level or higher. A lot of IT groups think “security” means authentication, authorization and encryption (AAE), but these are often tick-boxes that rarely stop breaches. The internal threat surfaces of data streams or disk drives in a raidset in a data centre are not the threat surface of interest.
Cyber or Threat organizations must conduct internal investigations of IT, subcontractors and supply chains without implicating the innocent. Therefore, they are organizationally air-gapped from IT. Some surveys indicate up to 10% of IT is under investigation at any given time.
Deploying a signal processing platform, such as Confluent Platform, allows organizations to evaluate data as soon as it becomes available enabling them to assess and mitigate risk before it arises. In Cyber or Threat Intelligence, events can be considered signals, and when analysts are hunting for threat actors, these don't appear as a single needle in a haystack, but as a series of needles. In this paradigm, streams of signals aggregate into signatures. This session shows how various sub-systems in Apache Kafka can be used to aggregate, integrate and attribute these signals into signatures of interest.
In this talk you will learn:
-The current threat landscape
-The difference between Security and Threat Intelligence
-The value of Confluent platform as an ideal complement to hardware endpoint detection systems and batch-based SIEM warehouses
Moving 150 TB of data resiliently on Kafka With Quorum Controller on Kubernet...
At Wells-Fargo, we move 150 TB of logs data from our syslogs to Splunk forwarders that get indexed and organized for analytic queries. As we modernize and migrate our applications to our hybrid cloud the performance expectations for this infrastructure will proportionately increase. Those improvements include the resilience of the end to end infrastructure. First, we decoupled the applications from their logging interface through a loglibrary which split the streams of logs from their sources to KAFKA which routed them to two separate destinations Splunk and ELK respectively. We also used prometheus and grafana for monitoring the metrics. We also deployed KAFKA, Splunk, ELK, Prometheus and Grafana on the Kubernetes clusters. Confluent had released a version of KAFKA without Zookeeper and replaced its functionality with Quorum Controller. The Quorum-Controller version exhibited better disposability one of the 12factors that's important for Cloud-Nativeness. We packaged this version into a Kubernetes operator called Keda and deployed this for auto-scaling. We tested this to simulate the amount of logdata that we typically generate in production. Based on the above we have also implemented distributed tracing and help make it just as resilient. We will share our lessons learnt, the patterns and practices to modernize both our underlying runtime platforms and our applications with highly performing and resilient event-driven architectures.
Kafka Summit NYC 2017 - Venice: A Distributed Database on top of Kafka
Matthew Wise presented on Venice, a distributed database built on top of Kafka. Some key points:
- Venice uses Kafka for streaming ingest and provides a distributed key-value store.
- It supports versioned data pushes where a topic maps to a data version.
- The system mirrors data across many datacenters for redundancy and supports over 600 stores with 100+ TB of data pushed daily.
What's hot (20)
You Must Construct Additional Pipelines: Pub-Sub on Kafka at Blizzard
You Must Construct Additional Pipelines: Pub-Sub on Kafka at Blizzard
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
Nordstrom's Event-Sourced Architecture and Kafka-as-a-Service | Adam Weyant a...
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...
Bringing Streaming Data To The Masses: Lowering The “Cost Of Admission” For Y...
Bringing Streaming Data To The Masses: Lowering The “Cost Of Admission” For Y...
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
Druid + Kafka: transform your data-in-motion to analytics-in-motion | Gian Me...
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Maximize the Business Value of Machine Learning and Data Science with Kafka (...
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
Why Kafka Works the Way It Does (And Not Some Other Way) | Tim Berglund, Conf...
How a distributed graph analytics platform uses Apache Kafka for data ingesti...
How a distributed graph analytics platform uses Apache Kafka for data ingesti...
Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...
Testing Event Driven Architectures: How to Broker the Complexity | Frank Kilc...
Cloud-Based Event Stream Processing Architectures and Patterns with Apache Ka...
Cloud-Based Event Stream Processing Architectures and Patterns with Apache Ka...
Self-service Events & Decentralised Governance with AsyncAPI: A Real World Ex...
Self-service Events & Decentralised Governance with AsyncAPI: A Real World Ex...
Simplifying Event Streaming: Tools for Location Transparency and Data Evoluti...
Simplifying Event Streaming: Tools for Location Transparency and Data Evoluti...
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
Keynote: Jay Kreps, Confluent | Kafka ♥ Cloud | Kafka Summit 2020
Death of the dumb pipes: Using Apache Kafka® for Integration projects
Death of the dumb pipes: Using Apache Kafka® for Integration projects
SIEM Modernization: Build a Situationally Aware Organization with Apache Kafka®
SIEM Modernization: Build a Situationally Aware Organization with Apache Kafka®
Moving 150 TB of data resiliently on Kafka With Quorum Controller on Kubernet...
Moving 150 TB of data resiliently on Kafka With Quorum Controller on Kubernet...
Kafka Summit NYC 2017 - Venice: A Distributed Database on top of Kafka
Kafka Summit NYC 2017 - Venice: A Distributed Database on top of Kafka
Similar to Sub-Second SQL Search, Aggregations and Joins with Kafka and Rockset | Dhruba Borthakur, Rockset
From Batch to Streaming ET(L) with Apache Apex
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
* Pipeline functionality from event source through queryable state for real-time insights.
* API for application development and development process.
* Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
* Stateful processing with event time windowing.
* Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
* Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
* Who is using Apex in production, and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
From Batch to Streaming with Apache Apex Dataworks Summit 2017
This document discusses transitioning from batch to streaming data processing using Apache Apex. It provides an overview of Apex and how it can be used to build real-time streaming applications. Examples are given of how to build an application that processes Twitter data streams and visualizes results. The document also outlines Apex's capabilities for scalable stream processing, queryable state, and its growing library of connectors and transformations.
From Batch to Streaming ET(L) with Apache Apex at Berlin Buzzwords 2017
https://berlinbuzzwords.de/17/session/batch-streaming-etl-apache-apex
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
Pipeline functionality from event source through queryable state for real-time insights.
API for application development and development process.
Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
Stateful processing with event time windowing.
Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
Recent project development and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
Structured Streaming in Spark
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‘.
Splunk App for Stream
Wire data provides deep insights across IT, security and business use cases by capturing the communications transmitted over the wire between machines and applications in real-time. The Splunk App for Stream enables new operational intelligence by indexing this wire data without needing instrumentation. It provides enhanced visibility, efficient cloud-ready collection, and fast time to value through interface-driven deployment. Key features include protocol decoding, attribute filtering, aggregations, and custom content extraction for analysis in Splunk.
Real Time Insights for Advertising Tech
A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several Data centers across the world. In batch data processing, data is collected at different geographic locations and processed at regular intervals. This system brings delay of at least 1 hour before an event is accounted for.
The goal of having real time streaming was to provide publishers, Demand Side Platforms (DSP's) and agencies actionable insights in a few minutes from the time of event generation.
This Ad Tech company uses DataTorrent RTS powered by Apex for:
• Real time reporting
• Resource monitoring
• Real time learning
• Allocation engine
Tushar Gosavi from DataTorrent will take the audience through the architecture, custom operators developed, use cases for real time and the challenges involved in implementing streaming systems at scale where multiple data centers are in play.
Tushar is a Senior Engineer at DataTorrent and has worked in distributed systems and storage domains.
Building end to end streaming application on Spark
This document discusses building a real-time streaming application on Spark to analyze sensor data. It describes collecting data from servers through Flume into Kafka and processing it using Spark Streaming to generate analytics stored in Cassandra. The stages involve using files, then Kafka, and finally Cassandra for input/output. Testing streaming applications and redesigning for testability is also covered.
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...
This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
xGem Data Stream Processing
This document provides an overview of data stream processing. It discusses what streaming data is and examples like IoT sensors, social media, and website monitoring. It also outlines the typical components of a streaming data pipeline including collecting and ingesting data from various sources, processing the data in real-time, storing the processed data, and serving it for analytics, search, and dashboards. Key streaming technologies mentioned include Apache Kafka, Apache NiFi, and various stream processing frameworks. It also introduces Stishovite as a console for managing an entire streaming data platform built from open source components.
Extracting Insights from Data at Twitter
Prasad Wagle's talk discussed how Twitter extracts insights from its large volumes of data. Twitter collects hundreds of millions of tweets and interactions per day from over 300 million monthly active users, creating big data challenges around velocity, volume, and variety. Twitter stores this data in hundreds of petabytes across large Hadoop clusters and processes it using batch tools like Hadoop and Spark as well as real-time tools like Heron. Insights are generated through basic analytics like user counts, A/B testing of new features, and custom data science work including machine learning models for recommendations, content filtering, and ad targeting. Systems, programming, and statistical skills are needed to effectively extract value from Twitter's big data.
AmazonRedshift
Amazon Redshift is a data warehousing tool developed by Amazon that allows for storing and analyzing large amounts of data. It uses a columnar data storage model and massively parallel processing to enable extremely fast queries. Redshift can be easily accessed through a web service API and supports standard SQL syntax. It is a cloud-based solution that provides high security and scalability for analytical workloads.
ClickHouse Paris Meetup. Pragma Analytics Software Suite w/ClickHouse, by Mat...
Pragma Innovation is an IT services company focused on time series data solutions. Their PASS (Pragma Analytics Software Suite) allows companies to analyze, report on, and make decisions from time series network data using open source software. It is designed for ISPs, hosting providers, and telecom companies. The solution ingests network and log data, standardizes it, enriches it using tools like GeoIP, and stores it in a time series database. This allows customers to build applications for traffic engineering, security, and business intelligence use cases. Key challenges addressed in version 2.0 of the solution include data sampling, IPv4/IPv6 support, and using ClickHouse as the time series database for its performance and simplicity
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
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.
Building a reliable and cost effect logging system at Box
See how Box used learnings from building an auditing and reporting system on Elasticsearch to address the big challenge of developing a robust and reliable logging solution with cost efficiencies in mind.
Sparking up Data Engineering: Spark Summit East talk by Rohan Sharma
Learn about the Big Data Processing ecosystem at Netflix and how Apache Spark sits in this platform. I talk about typical data flows and data pipeline architectures that are used in Netflix and address how Spark is helping us gain efficiency in our processes. As a bonus – i’ll touch on some unconventional use-cases contrary to typical warehousing / analytics solutions that are being served by Apache Spark.
Introduction to Apache Apex
Introduction to Apache Apex presented by Priyanka Gugale: Committer Apache Apex, DataTorrent engineer
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...
Over 137 million members worldwide are enjoying TV series, feature films across a wide variety of genres and languages on Netflix. It leads to petabyte scale of user behavior data. At Netflix, our client logging platform collects and processes this data to empower recommendations, personalization and many other services to enhance user experience. Built with Apache Flink, this platform processes 100s of billion events and a petabyte data per day, 2.5 million events/sec in sub milliseconds latency. The processing involves a series of data transformations such as decryption and data enrichment of customer, geo, device information using microservices based lookups.
The transformed and enriched data is further used by multiple data consumers for a variety of applications such as improving user-experience with A/B tests, tracking application performance metrics, tuning algorithms. This causes redundant reads of the dataset by multiple batch jobs and incurs heavy processing costs. To avoid this, we have developed a config driven, centralized, managed platform, on top of Apache Flink, that reads this data once and routes it to multiple streams based on dynamic configuration. This has resulted in improved computation efficiency, reduced costs and reduced operational overhead.
Stream processing at scale while ensuring that the production systems are scalable and cost-efficient brings interesting challenges. In this talk, we will share about how we leverage Apache Flink to achieve this, the challenges we faced and our learnings while running one of the largest Flink application at Netflix.
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Apresentação para a Agência Nacional de Aviação Civil sobre Otimzações de Projetos de Big Data, Dw e AI
Stream Processing with Flink and Stream Sharing
Stream Processing with Apache Flink in the Cloud and Stream Sharing
Kai Waehner discusses stream processing use cases with Apache Flink and introduces Confluent's plans to offer a serverless Apache Flink service in Confluent Cloud. He also discusses Confluent Stream Sharing, which allows for easy and secure sharing of Kafka topics across organizations in real-time.
[WSO2Con EU 2018] The Rise of Streaming SQL
This session takes an in-depth look at:
- Trends in stream processing
- How streaming SQL has become a standard
- The advantages of Streaming SQL
- Ease of development with streaming SQL: Graphical and Streaming SQL query editors
- Business value of streaming SQL and its related tools: Domain-specific UIs
- Scalable deployment of streaming SQL: Distributed processing
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"In this talk, attendees will be provided with an introduction to Kafka Connect and the basics of Single Message Transforms (SMTs) and how they can be used to transform data streams in a simple and efficient way. SMTs are a powerful feature of Kafka Connect that allow custom logic to be applied to individual messages as they pass through the data pipeline. The session will explain how SMTs work, the types of transformations they can be used for, and how they can be applied in a modular and composable way.
Further, the session will discuss where SMTs fit in with Kafka Connect and when they should be used. Examples will be provided of how SMTs can be used to solve common data integration challenges, such as data enrichment, filtering, and restructuring. Attendees will also learn about the limitations of SMTs and when it might be more appropriate to use other tools or frameworks.
Additionally, an overview of the alternatives to SMTs, such as Kafka Streams and KSQL, will be provided. This will help attendees make an informed decision about which approach is best for their specific use case.
Whether attendees are developers, data engineers, or data scientists, this talk will provide valuable insights into how Kafka Connect and SMTs can help streamline data processing workflows. Attendees will come away with a better understanding of how these tools work and how they can be used to solve common data integration challenges."
Renaming a Kafka Topic | Kafka Summit London
"While Apache Kafka lacks native support for topic renaming, there are scenarios where renaming topics becomes necessary. This presentation will delve into the utilization of MirrorMaker 2.0 as a solution for renaming Kafka topics. It will illustrate how MirrorMaker 2.0 can efficiently facilitate the migration of messages from the old topic to the new one and how Kafka Connect Metrics can be employed to monitor the mirroring progress. The discussion will encompass the complexity of renaming Kafka topics, addressing certain limitations, and exploring potential workarounds when using MirrorMaker 2.0 for this purpose. Despite not being originally designed for topic renaming, MirrorMaker 2.0 has a suitable solution for renaming Kafka topics.
Blog Post : https://engineering.hellofresh.com/renaming-a-kafka-topic-d6ff3aaf3f03"
Evolution of NRT Data Ingestion Pipeline at Trendyol
"Trendyol, Turkey's leading e-commerce company, is committed to positively impacting the lives of millions of customers. Our decision-making processes are entirely driven by data. As a data warehouse team, our primary goal is to provide accurate and up-to-date data, enabling the extraction of valuable business insights.
We utilize the benefits provided by Kafka and Kafka Connect to facilitate the transfer of data from the source to our analytical environment. We recently transitioned our Kafka Connect clusters from on-premise VMs to Kubernetes. This shift was driven by our desire to effectively manage rapid growth(marked by a growing number of producers, consumers, and daily messages), ensuring proper monitoring and consistency. Consistency is crucial, especially in instances where we employ Single Message Transforms to manipulate records like filtering based on their keys or converting a JSON Object into a JSON string.
Monitoring our cluster's health is key and we achieve this through Grafana dashboards and alerts generated through kube-state-metrics. Additionally, Kafka Connect's JMX metrics, coupled with NewRelic, are employed for comprehensive monitoring.
The session will aim to explain our approach to NRT data ingestion, outlining the role of Kafka and Kafka Connect, our transition journey to K8s, and methods employed to monitor the health of our clusters."
Ensuring Kafka Service Resilience: A Dive into Health-Checking Techniques
"Join our lightning talk to delve into the strategies vital for maintaining a resilient Kafka service.
While proactive monitoring is key for issue prevention, failures will still occur. Rapid detection tools will enable you to identify and resolve problems before they impact end-users. This session explores the techniques employed by Kafka cloud providers for this detection, many of which are also applicable if you are managing independent Kafka clusters or applications.
The talk focuses on health-checking, a powerful tool that encompasses an application and its monitoring to validate Kafka environment availability. The session navigates through Kafka health-check methods, sharing best practices, identifying common pitfalls, and highlighting the monitoring of critical performance metrics like throughput and latency for early issue detection.
Attendees will gain valuable insights into the art of health-checking their Kafka environment, equipping them with the tools to identify and address issues before they escalate into critical problems. We invite all Kafka enthusiasts to join us in this talk to foster a deeper understanding of Kafka health-checking and ensure the continued smooth operation of your Kafka environment."
Exactly-once Stream Processing with Arroyo and Kafka
"Stream processing systems traditionally gave their users the choice between at least once processing and at most once processing: accepting duplicate data or missing data. But ideally we would provide exactly-once processing, where every event in the input data is represented exactly once in the output.
Kafka provides a transaction API that enables exactly-once when using Kafka as your source and sink. But this API has turned out to not be well suited for use by high level streaming systems, requiring various work arounds to still provide transactional processing.
In this talk, I’ll cover how the transaction API works, and how systems like Arroyo and Flink have used it to build exactly-once support, and how improvements to the transactional API will enable better end-to-end support for consistent stream processing."
Fish Plays Pokemon | Kafka Summit London
"In this talk, we will explore the exciting world of IoT and computer vision by presenting a unique project: Fish Plays Pokemon. Using an ESP Eye camera connected to an ESP32 and other IoT devices, to monitor fish's movements in an aquarium.
This project showcases the power of IoT and computer vision, demonstrating how even a fish can play a popular video game. We will discuss the challenges we faced during development, including real-time processing, IoT device integration, and Kafka message consumption.
By the end of the talk, attendees will have a better understanding of how to combine IoT, computer vision, and the usage of a serverless cloud to create innovative projects. They will also learn how to integrate IoT devices with Kafka to simulate keyboard behavior, opening up endless possibilities for real-time interactions between the physical and digital worlds."
Tiered Storage 101 | Kafla Summit London
What is tiered storage and what is it good for? After this session you will know how to leverage the tiered storage feature to enable longer retention than the storage attached to brokers allows. You will get acquainted with the different configuration options and know what to expect when you enable the feature, like for example when will the first upload to the remote object storage take place.
Building a Self-Service Stream Processing Portal: How And Why
"Real-time 24/7 monitoring and verification of massive data is challenging – even more so for the world’s second largest manufacturer of memory chips and semiconductors. Tolerance levels are incredibly small, any small defect needs to be identified and dealt with immediately. The goal of semiconductor manufacturing is to improve yield and minimize unnecessary work.
However, even with real-time data collection, the data was not easy to manipulate by users and it took many days to enable stream processing requests – limiting its usefulness and value to the business.
You’ll hear why SK hynix switched to Confluent and how we developed a self-service stream process portal on top of it. Now users have an easy-to-use service to manipulate the data they want.
Results have been impressive, stream processing requests are available the same day – previously taking 5 days! We were also able to drive down costs by 10% as stream processing requests no longer require additional hardware.
What you’ll take away from our talk:
- What were the pain points in the previous environment
- How we transitioned to Confluent without service downtime
- Creating a self-service stream processing portal built on top of Connect and ksqlDB
- Use case of stream process portal"
From the Trenches: Improving Kafka Connect Source Connector Ingestion from 7 ...
"Discover how default configurations might impact ingestion times, especially when dealing with large files. We'll explore a real-world scenario with a 20,000,000+ line file, assessing metrics and exploring the bottleneck in the default setup. Understand the intricacies of batch size calculations and how to optimize them based on your unique data characteristics.
Walk away with actionable insights as we showcase a practical example, turning a 7-hour ingestion process into a mere 30 minutes for over 30,000,000 records in a Kafka topic. Uncover metrics, configurations, and best practices to elevate the performance of your Kafka Connect CSV source connectors. Don't miss this opportunity to optimize your data pipeline and ensure smooth, efficient data flow."
Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...
"In order to meet the current and ever-increasing demand for near-zero RPO/RTO systems, a focus on resiliency is critical. While Kafka offers built-in resiliency features, a perfect blend of client and cluster resiliency is necessary in order to achieve a highly resilient Kafka client application.
At Fidelity Investments, Kafka is used for a variety of event streaming needs such as core brokerage trading platforms, log aggregation, communication platforms, and data migrations. In this lightening talk, we will discuss the governance framework that has enabled producers and consumers to achieve their SLAs during unprecedented failure scenarios. We will highlight how we automated resiliency tests through chaos engineering and tightly integrated observability dashboards for Kafka clients to analyze and optimize client configurations. And finally, we will summarize the chaos test suite and the ""test, test and test"" mantra that are helping Fidelity Investments reach its goal of a future with zero down-time."
Navigating Private Network Connectivity Options for Kafka Clusters
"There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options ... might not be an option!
In this session, we’ll discuss how you can use SSH bastions or a self managed PrivateLink endpoint to establish connectivity to your Kafka clusters without exposing brokers directly to the internet. We explain the required network configuration, and show how we at Materialize have contributed to librdkafka to simplify these scenarios and avoid fragile workarounds."
Apache Flink: Building a Company-wide Self-service Streaming Data Platform
"In my talk, we will examine all the stages of building our self-service Streaming Data Platform based on Apache Flink and Kafka Connect, from the selection of a solution for stateful streaming data processing, right up to the successful design of a robust self-service platform, covering the challenges that we’ve met.
I will share our experience in providing non-Java developers with a company-wide self-service solution, which allows them to quickly and easily develop their streaming data pipelines.
Additionally, I will highlight specific business use cases that would not have been implemented without our platform.0 characters0 characters"
Explaining How Real-Time GenAI Works in a Noisy Pub
"Almost everyone has heard about large language models, and tens of millions of people have tried out OpenAI ChatGPT and Google Bard. However, the intricate architecture and underlying mathematics driving these remarkable systems remain elusive to many.
LLM's are fascinating - so let's grab a drink and find out how these systems are built and dive deep into their inner workings. In the length of time it to enjoy a round of drinks, you'll understand the inner workings of these models. We'll take our first sip of word vectors, enjoy the refreshing taste of the transformer, and drain a glass understanding how these models are trained on phenomenally large quantities of data.
Large language models for your streaming application - explained with a little maths and a lot of pub stories"
TL;DR Kafka Metrics | Kafka Summit London
"Monitoring is a fundamental operation when running Kafka and Kafka applications in production. There are numerous metrics available when using Kafka, however the sheer number is overwhelming, making it challenging to know where to start and how to properly utilise them.
This session will introduce you to some of the key metrics that should be monitored and best practices in fine tuning your monitoring. We will delve into which metrics are the indicators for cluster’s availability and performance and are the most helpful when debugging client applications."
A Window Into Your Kafka Streams Tasks | KSL
Kafka Streams relies on state restoration for maintaining standby tasks as failure recovery mechanism as well as for restoring the state after rebalance scenarios. When you are scaling up or down your application instances, it is necessary to know the current state of the restoration process for each active and standby task in order to prevent a long restoration process as much as possible. During this presentation, you will get an understanding of how KIP-869 provides valuable information about the current active task restoration after a rebalance and KIP-988 opens a window to the continuous process of standby restoration. When you encounter a situation in which you need to choose whether or not to scale up or down your application instances, both KIPs will be an invaluable ally for you.
Mastering Kafka Producer Configs: A Guide to Optimizing Performance
"In this talk, we will dive into the world of Kafka producer configs and explore how to understand and optimize them for better performance. We will cover the different types of configs, their impact on performance, and how to tune them to achieve the best results. Whether you're new to Kafka or a seasoned pro, this session will provide valuable insights and practical tips for improving your Kafka producer performance.
- Introduction to Kafka producer internal and workflow
- Understanding the producer configs like linger.ms, batch.size, buffer.memory and their impact on performance
- Learning about producer configs like max.block.ms, delivery.timeout.ms, request.timeout.ms and retries to make producer more resilient.
- Discuss configs like enable.idempotence, max.in.flight.requests.per.connection and transaction related configs to achieve delivery guarantees.
- Q&A session with attendees to address specific questions and concerns."
Data Contracts Management: Schema Registry and Beyond
"Data contracts are one of the hottest topics in the data management community. A data contract is a formal agreement between a data producer and its consumers, aimed at reducing data downtime and improving data quality. Schemas are an important part of data contracts, but they are not the only relevant element.
In this talk, we’ll:
1. see why data contracts are so important but also difficult to implement;
2. identify the characteristics of a well-designed data contract:
discuss the anatomy of a data contract, its main elements and, how to formally describe them;
3. show how to manage the lifecycle of a data contract leveraging Confluent Platform's services."
Code-First Approach: Crafting Efficient Flink Apps
"In the realm of stateful stream processing, Apache Flink has emerged as a powerful and versatile platform. However, the conventional SQL-based approach often limits the full potential of Flink applications.
We will delve into the benefits of adopting a code-first approach, which provides developers with greater control over application logic, facilitates complex transformations, and enables more efficient handling of state and time. We will also discuss how the code-first approach can lead to more maintainable and testable code, ultimately improving the overall quality of your Flink applications.
Whether you're a seasoned Flink developer or just starting your journey, this talk will provide valuable insights into how a code-first approach can revolutionize your stream processing applications."
Debezium vs. the World: An Overview of the CDC Ecosystem
"Change Data Capture (CDC) has become a commodity in data engineering, much in part due to the ever-rising success of Debezium [1]. But is that all there is? In this lightning talk, we’ll outline the current state of the CDC ecosystem, and understand why adopting a Debezium alternative is still a hard sell. If you’ve ever wondered what else is out there, but can’t keep up with the sprawling of new tools in the ecosystem; we’ll wrap it up for you!
[1] https://debezium.io/"
Beyond Tiered Storage: Serverless Kafka with No Local Disks
"Separation of compute and storage has become the de-facto standard in the data industry for batch processing.
The addition of tiered storage to open source Apache Kafka is the first step in bringing true separation of compute and storage to the streaming world.
In this talk, we'll discuss in technical detail how to take the concept of tiered storage to its logical extreme by building an Apache Kafka protocol compatible system that has zero local disks.
Eliminating all local disks in the system requires not only separating storage from compute, but also separating data from metadata. This is a monumental task that requires reimagining Kafka's architecture from the ground up, but the benefits are worth it.
This approach enables a stateless, elastic, and serverless deployment model that minimizes operational overhead and also drives inter-zone networking costs to almost zero."
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- 7. How Kafka and Rockset Work Together 7 Events from apps, devices, sensors KSQL Enrichment Real-time analytics applications OLTP database or data lake SQL, REST
- 8. An indexing database to serve queries from Kafka data
- 9. Query Latency ● Ad-hoc queries and drilldowns in real-time ● Millisecond-latency queries to support live dashboards and data APIs ● How to get achieve low-latency queries? 9
- 10. Optimize Query Latency by Indexing Traditional approach: Parallelize and scan 10 event data MapReduce reports event data Converged Indexing ad hoc analytics Real-time analytics: Parallelize and index Column store Column, Inverted and Row store
- 11. ● All fields are indexed in inverted, columnar and row indexes ● Accelerates search, aggregation and join queries ● No index definition required Converged Index <doc 0> { “name”: “Igor” } <doc 1> { “name”: “Dhruba” } Key Value R.0.name Igor Row Store R.1.name Dhruba C.name.0 Igor Column Store C.name.1 Dhruba S.name.Dhruba.1 Search index S.name.Igor.0 11
- 12. 12 Query Optimizer ● Low latency for both highly selective queries and large scans ● Optimizer picks between ○ inverted index (Index Filter operator) ○ columnar format (Column Scan operator) ○ inverted index (Index Scan operator)
- 13. Advantages of indexing Kafka data
- 14. Complex Queries ● Support for expressive query language ● Ability to perform joins, aggregations, sorting, filtering, etc. 14
- 15. Read-Time JOINs ● Streams are most useful when joined with other data 15 Streaming event data Query Analytics backend Other Data Sources (e.g. Amazon S3)
- 16. Flexibility with Data and Schema ● Allow values of different types in the same column ● Ability to ingest new data without needing data cleaning at write time ○ Avoid flattening or denormalization for performance reasons ● Type binding not done at write time (but done later at query time) 16
- 18. Disaggregated, Cloud-Native Architecture 18 Aggregator Leaf Tailer Architecture
- 19. 19 Scaling Query Compute Aggregator Leaf Tailer Architecture
- 20. Continuous Ingestion and Indexing from Kafka ● Fast ingestion ○ New data is visible in query results in seconds ○ Complex ETL processes can add minutes to hours before the data is available to query ● Live sync ○ Continuous sync of new data from Kafka 20 live sync within seconds
- 21. Ingest Rollups ● SQL rollups and transformations ○ Pre-aggregate data at ingest time to increase performance and reduce size ○ Familiar SQL syntax 21
- 22. Demo
- 23. Learn More 23 Request demo or get started for free at rockset.com Reach out at dhruba@rockset,com
- 24. Thank you Dhruba Borthakur / dhruba@rockset.com
- 25. 25 ● Fields are dynamically typed Strong Dynamic Typing
- 26. 26 ● Fields are dynamically typed ● Queries are strongly typed Strong Dynamic Typing
- 27. 27 ● Fields are dynamically typed ● Queries are strongly typed ● Smart schemas Strong Dynamic Typing