Streaming is a Detail
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"We can all agree that streaming is super cool. And for a while now, the adoption conversation has been largely led with an all-in mentality. But that’s silly. The only concerns end users have are: -The freshness of their data -Latency they require to meet their SLAs from source to consumption -All while maintaining data quality and governance. Luckily, the industry has realized this and we have seen a shift of streaming capabilities surfacing as an in-database technology, via objects as trivial to analytics engineers as views - materialized that is. With this convergence of streaming capabilities and batch level accessibility, this is when ELT tools like dbt can join in and expand out the adoption story. dbt is the T in ELT, Extract Load and Transform. In dbt, analytics engineers design models - SQL (and occasional python) statements that encapsulate business logic. At runtime, dbt will wrap that logic in a DDL statement and send it over to the data platform to execute. In this session, we’ll discuss how we see streaming at dbt Labs. We will dive into how we are extending dbt to support low-latency scenarios and the recent additions we have made to make batch and streaming allies in a DAG rather than archenemies."
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The availability of new tools in the modern data stack is changing the way data teams operate. Specifically, the modern data stack supports an “ELT” approach for managing data, rather than the traditional “ETL” approach. In an ELT approach, data sources are automatically loaded in a normalized state into Delta Lake and opinionated transformations happen in the data destination using dbt. This workflow allows data analysts to move more quickly from raw data to insight, while creating repeatable data pipelines robust to changes in the source datasets. In this presentation, we’ll illustrate how easy it is for even a data analytics team of one to to develop an end-to-end data pipeline. We’ll load data from GitHub into Delta Lake, then use pre-built dbt models to feed a daily Redash dashboard on sales performance by manager, and use the same transformed models to power the data science team’s predictions of future sales by segment.
DBT ELT approach for Advanced Analytics.pptx
*Event*
DBT (Data Build Tool) an ELT approach for Advanced Analytics (wearecommunity.io)
https://wearecommunity.io/events/dbt-data-build-tool-an-elt-approach-for-advanced-analytics
*Demo*
Goal: calculate monthly sales values by category
Tech stacks: DBT, Databricks, Azure Blob
Data: Brazilian E-Commerce Public Dataset by Olist (Kaggle)
Github: https://github.com/ongxuanhong/de05-dbt-databricks
YouTube: https://youtu.be/l4Mug-Qp3ag
Bridging the Last Mile: Getting Data to the People Who Need It
Watch full webinar here: https://bit.ly/3cUA0Qi
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Ajith_kumar_4.3 Years_Informatica_ETL
The candidate has over 4 years of experience as an ETL developer using Informatica. They have extensive experience developing mappings using transformations like aggregator, lookup, filter and joiner. They have worked on multiple projects in the telecommunications domain for clients like British Telecom extracting data from various sources and loading to data warehouses. Their skills include Informatica, Oracle, SQL and they have experience in requirements analysis, mapping development, testing and support.
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Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
"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.
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Fish Plays Pokemon | Kafka Summit London
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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
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- 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.
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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."
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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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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
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TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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Streaming is a Detail
- 1. Streaming is a Detail Current 2023 Amy Chen & Florian Eiden 1
- 2. 2 Introductions Amy Chen Staff Partner Engineer Fun fact: Iʼve made dbt soap 🧼 Florian Eiden Staff Product Manager Fun fact: I live on an 🏝
- 3. Pieces of the puzzle - Transactional vs Analytical - Analytics Engineering vs Data Engineering - Personas - ELT vs ETL - Streaming in the ELT world (bring streaming to the database, or the other way around) - Operational Analytics - Why does it have to be Batch vs Streaming? - Is streaming the biggest trend in analytics? - What is the next big milestone for streaming? - How do we solve CI/CD? Testing? Replayability? - Is Flink a database now? - Is pipeline the right way to bundle logic? What about DAGs? - Logic: plumbing vs business logic
- 5. dbt is ELT 5
- 6. The dbt viewpoint: Build data like developers build applications 6 dbt uses testing, version control, reusable code, and documentation to get to the right answer, faster. Work like engineers Pairing code-based transformation with your favorite git provider means flexibility without chaos. Code Reigns
- 7. Write reusable & referenceable logic with SQL + Jinja
- 8. Infer lineage for automated dependency management.
- 9. 9 SQL-friendly + version control safely expands participants Reusable code speeds development Built-in CI/CD increases pipeline reliability Dependency management speeds troubleshooting 01 Visible lineage increases data understanding Testing and documentation increase data trust 02 03 04 05 06 Data Engineers, Analysts, and Data Scientists Collaborative Code Dashboard A = Dashboard B Automatic Documentation Analysts and Business Users
- 10. Leverage your existing cloud data platform, with out-of-the-box adapters to all major warehouses. Benefit from partnerships across the Modern Data Stack. 10 Data Quality Orchestration Data Ingestion Other Cloud Data Platform Analysis & Visualization Data Catalog & Active Metadata Operational Analytics Develop Test & Document Deploy
- 12. The questions on when to use MVs What are the costs associated with running the materialized view versus a batched incremental model? (this will vary depending on your data platform as some will require different compute nodes) Does your data platform support joins, aggregations, and window functions on MVs if you need them? What are the latency needs of your development environment? In production? (If not near real time, you can make the choice between a batch incremental model or a MV with a longer refresh schedule.) How often do your upstream dependencies update? If your answer is not frequent, you may not need a MV. How large is your dataset?(It might be cheaper to use MVs for extremely large datasets) How often do you need your query refreshed? What are your downstream dependencies and their stakeholders? (If near real time is important, MVs might be the right choice). Do you have real time machine learning models training or applications using your transformed dataset?