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Or, why and how I built a visibility-bitcoin exchange site. A few months ago, I saw this tweet. It is exactly what it looks like: an exchange rate tracker with a calculator to convert vizcoins to bitcoins because one fake currency can only translate to another — and also because it was harder to get actual updated forex rates than it was worth for a quick joke. Yes, but why? In part, because I didn’t want to just vent frustration on Twitter. Instead, I wanted to make something more tangible and visible (!) than an angry Internet post here and there. And in part, because I have been out of the web-development loop for a while, and I wanted to catch up with the Kardashians. Ok, so what did I do? First of all, nowadays if you are doing a statically rendered, server-side thing with just the bare minimum of JS on top, you are out. Instead, all the cool kids out there are separating backends from frontends, which is a thing that would normally fill me with joy because people are paying attention to stuff like the , right? Instead, in this case, it filled me with existential dread, but my engineering side kicked in and I decided to do things right instead of being lazy. finally separation of concerns Backend The problem was very simple: to make a backend API that would serve the exchange rates in standard OHLC format, at hourly intervals. I spent a lot more time thinking about this than would have been reasonable. I have been looking forward to doing some server-side Swift pretty much since server-side Swift has been a thing, so the backend language was not even a question. The question was regarding which framework I was going to use. Right now, there are a bunch of alternatives, the most popular being , , , and , and there are tons of comparison posts all over the Internet. Kitura Perfect Zewo Vapor In my case, I needed an API with a single endpoint that served a set of numbers in JSON format, so I was not too concerned with performance. I went for Vapor first, but , so I fell back on Kitura because ) it’s backed-up by IBM, and ) I didn’t particularly like Perfect, and Zewo seemed too much like the newest cool kid on the block, so it didn’t quite inspire trustworthiness to me. I’m not the greatest judge of character, though. 😏 couldn’t get it running on my Mac because of Homebrew a b Overall, I am very pleased with my API. I like Swift, and Kitura is a nice enough framework to work with. I wanted to move quickly and get things up and running, so I didn’t want to spend days elbow-deep in documentation. This meant that my code has a few kinks that need working out, and I am still confused about a few things, like what the heck happened to execution environment variables and debug/release configurations, or when will Xcode properly integrate with the Swift Package Manager, or are we ever going to see a decent third-party IDE fully support Swift and the SPM? These are the important questions in the life of a server-side Swift developer. The next important question, one that I could answer, was: how do I generate plausible data? I mean, despite traders swearing on their mothers that , the truth is that, —I mean, it’s gambling, after all. So, given that I resolved to only show a graph 48 hours long, presenting hourly intervals, I decided to use a Perlin noise generator, and it’s doing a pretty good job. Take that, forex. trading is not a random walk at least locally, it probably is a random walk You can have a look at the source code in my Github account. API Data generator and other tools Frontend The last time I did web frontend work on my own, LESS was all the rage, jQuery was still the go-to library for all things Javascript, and you wanted to do most of your computing on the server because clients were crappy and unpredictable. Yes, it’s been a while, now Sass is all the rage (or is it Next? or is it Pure? or is it…), there have probably been three or four JS frameworks born in the time I typed this post, and browsers are still crappy and unpredictable, although they are slowly getting better. In my current job, we manage a large JS-heavy web site based on Angular 1 (!) and React and stuff, with a segregated API in Java — yes, I know. So, I figured, I’ll give this ECMAScript 2015+ fad a go. For the , I used . At first, I used , but then things turned out a bit more complex than I expected, so I figured I’d try D3, given its popularity. Maybe I’m missing something, but I think I ended up manually coding an SVG document from scratch, using that very poor excuse for data binding that apparently makes D3 what it is. I literally had to take a shower afterwards. graph D3 P5 With the graph done — and not even like I wanted it to be — I moved on to the . React was kinda the obvious choice here. I use it at work, it’s pretty neat, the whole JSX thing is gross but then again JS is gross, so overall I just went with it and was not even entirely disgusted. Updating the component’s state confuses me to no end, but in the end, I managed to get it working. exchange calculator , I used . I am honestly not sure what is going on when I run it. I guess I was expecting something more like a GNU Make plus a kind of a file watch thing, something like a configurable pipeline of building minions that would neatly do what I want them to do. I’m not sure I’ll be using it for my next project, unless it makes absolutely sense, or someone forces it down my throat. Even at work, we gave up on it, though I’m not sure what we replaced it with. 😛 To tie everything up together Webpack . Have a look at the source code, if you dare Deployment and HTTPS HTTPS is cool, it’s important, and it’s a no-brainer with , if you have your own private server, or an open-minded ISP. Let’s Encrypt Deployment was also quite easy with Docker on my own VPS. In conclusion VIZex.co was a fun project. I can’t say I learned a lot, but at least I got my feet wet, and I have now a decent platform to explore modern web-development further — though I remain skeptical, but that’s probably just me and the type of software that I normally develop. Ironically, I am doing this for the exposure, so if you liked this post and VIZex, give it a round of applause here on Medium, and spread the word among your freelance friends. Hopefully, the next time someone offers you visibility in exchange for work, you will point them at and ask them for an adequate amount of bitcoins instead — or the equivalent in your own preferred currency! 😜 VIZex.co , I’m doing this for fun and for free, but my ISP, quite reasonably, asks for money. If you could spare a few coins, I’d greatly appreciate it. Just scroll down on ! Speaking of coins VIZex.co And if you are wondering why I spelled #finntech with two ‘n’s, that’s because it stands for Finnish Technology. 😅 Hey, I have some other stories here on Medium, including How I messed up my artsy app’s launch How I made an artsy app based on science The startup scene has to take a long, hard look at itself Has the freedom of movement in Europe been revoked recently?

The #VIZcoin revolution

The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 12 of 15. HackerNoon’s Legal PDF Series here B. The Complaint Fails to State a Contributory Infringement Claim Count IV attempts to hold OpenAI liable for “materially contribut[ing] to and directly assist[ing] with the direct infringement perpetrated by end-users of the GPT-based products.” Compl. ¶ 179. This claim relies on the doctrine of “contributory infringement,” a species of secondary liability that the Supreme Court has defined by reference to the Patent Act. 35 U.S.C. § 271(b); Sony, 464 U.S. at 435–41. To plead it, a plaintiff must allege: “(1) direct infringement by a third party, (2) that the defendant had knowledge of the infringing activity, (3) and that the defendant materially contributed to the third party’s infringement.” Dow Jones & Co., Inc. v. Juwai Ltd., No. 21-cv-7284, 2023 WL 2561588, at *3 (S.D.N.Y. Mar. 17, 2023) (cleaned up). Here, the acts of “direct infringement” alleged are the example outputs from the Complaint, discussed above. See supra 12. To proceed with Count IV, the Times must allege that OpenAI “had knowledge” of the Times’s creation of those outputs. Dow Jones, 2023 WL 2561588, at *3. A defendant cannot be contributorily liable without “culpable intent,” and courts may not “imput[e] intent” solely based on the “characteristics or uses of a [] product.” Metro-GoldwynMayer Studios Inc. v. Grokster, Ltd., 545 U.S. 913, 934 (2005). As the Supreme Court explained in Commil USA, LLC v. Cisco Sys., Inc., contributory patent infringement requires more than allegations that a defendant knew there “might” be infringement. 575 U.S. 632, 642 (2015). Instead, a plaintiff must allege that the defendant had actual knowledge of specific infringements. Courts have consistently applied this same approach to copyright claims. Pleading a contributory copyright claim requires allegations that the defendant either had “actual knowledge of specific acts of infringement” or “took deliberate actions to avoid learning about the infringement.” Luvdarts LLC v. AT & T Mobility, LLC, 710 F.3d 1068, 1072 (9th Cir. 2013).[34] As the Fourth Circuit recently explained, this follows directly from Commil, as well as other foundational Supreme Court cases like Sony, 464 U.S. 417, and Grokster, 545 U.S. 913. See BMG Rights Mgmt. (US) LLC v. Cox Commc’ns, Inc., 881 F.3d 293, 308–10 (4th Cir. 2018) (allegations that defendant “should have known” insufficient). Courts in this district agree, rejecting allegations that defendants are “general[ly] aware[] that there are infringements” as insufficient.[35] Here, the only allegation supporting the Times’s contributory claim states that OpenAI “had reason to know of the direct infringement by end-users” because of its role in “developing, testing, and troubleshooting” its products. Compl. ¶ 180. But “generalized knowledge” of “the possibility of infringement” is not enough. Luvdarts, 710 F.3d at 1072. The Complaint does not allege OpenAI “investigated or would have had reason to investigate” the use of its platform to create copies of Times articles. Popcornflix.com, 2023 WL 571522, at *6. Nor does it suggest that OpenAI had any reason to suspect this was happening. Indeed, OpenAI’s terms expressly prohibit such uses of its services. Supra note 8. And even if OpenAI had investigated, nothing in the Complaint explains how it might evaluate whether these outputs were acts of copyright infringement or whether their creation was authorized by the copyright holder (as they were here). Continue Reading . Here [34] The Second Circuit adopted this rule in Viacom Int’l, Inc. v. YouTube, Inc. for the parallel standard under 17 U.S.C. § 512. 676 F.3d 19, 35 (2d Cir. 2012) (requiring actual knowledge or “deliberate effort to avoid [] knowledge”). It also used a similar rule in the Lanham Act context in Tiffany (NJ) Inc. v. eBay Inc., 600 F.3d 93, 107 (2d Cir. 2010) (“contributory [] liability” requires “contemporary knowledge of which particular [acts] are infringing”). [35] Lefkowitz v. John Wiley & Sons, No. 13-cv-6414, 2014 WL 2619815, at *11 (S.D.N.Y. June 2, 2014) (dismissing claim); State Street Global Advisors Trust Co. v. Visbal, 431 F. Supp. 3d 322, 358 (S.D.N.Y. 2020) (same); see also Hartmann v. Popcornflix.com LLC, No. 20-cv-4923, 2023 WL 5715222, at *6 (S.D.N.Y. Sept. 5, 2023) (dismissing for failure to plead defendant “would have had reason to investigate the [] infringement”); Hartmann v. Apple, Inc., No. 20-cv-6049-GHW, 2021 WL 4267820, at *7 (S.D.N.Y. Sept. 20, 2021) (“general ability to discover” insufficient). About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. fingfx.thomsonreuters.com

The New York Times Fails to Explain How ChatGPT's Responses Were Acts of Copyright Infringement

Spoiler: Use to render DAGs written in the DOT language Vizdom.dev As a software dev, I’ve occasionally worked with DAGs — most of which involve visualizing them. In many cases, I found it difficult to get a quick rendering and found myself looking for tooling. Fortunately, there’s the trusty tool that can render graphs written in the DOT language — Perfect! Graphviz Except… It doesn’t work well on the web* I need to install it It doesn’t manage DOT/GV files online Considerations *Okay, Graphviz can be compiled to WebAssembly, and a few talented folks managed to build a few neat projects such as dreampuf’s , nikeee’s , and magjac’s — each with its own twist. I still struggle to embed these renderings without exporting them to an SVG (or other medium). technically editor edotor.net editor For example, I want to store my graph in by simply pasting a link that automatically renders the graph. Notion.so I could use which renders graphs pretty cleanly and works surprisingly well — it’s one of the many reasons why it has . I can also embed graphs in Notion/GitHub by using markdown, sweet! MermaidJS GitHub 64k+ starts on Unfortunately, I have to translate DOT into the flavor of markdown that Mermaid requires. For smaller textual representations, I can use ChatGPT to convert them for me, but it frequently makes grammatical mistakes, and the graph will refuse to render — making it unreliable as a source of automation. Then there is which is amazing for managing diagrams, but like my Mermaid woes, I cannot convert DOT to D2 reliably. I suppose if you’re going to be building diagrams by hand this is an acceptable, one-time cost. In general, it is a fantastic tool. Kudos! Terrastruct.com hand-built The Problem I want to render generated DOT outputs — none of the tooling seems to help me achieve that goal. programmatically I want to embed links that render graphs. I want to store the rendering online for updates or to visually compare it with other graphs. The Solution Yup — I built an for that 🎉 app …And it’s built entirely in Rust 🦀 with many thanks to Leptos tl;dr — a simple, purpose-built app to render DAGs online, fast Minimizing edge crossings in graph generation, a recognized NP-hard challenge is key to creating visually appealing graphs. The team at Terrastruct has published an outstanding delving into this topic. Inspired significantly by the DagreJS layout algorithm(s) and the insightful paper “ ¹”, I developed a variant in Rust. blog post A Technique for Drawing Directed Graphs This version is particularly effective for rendering hierarchical graphs, leveraging the intricate complexities of this problem. Rendering large DAGs (500+ nodes/edges) tends to be somewhat sluggish with Graphviz. Dagre + D3 ( and derivatives) end up quite a bit faster and offer great animations/styling customizations. Yet, I felt that they miss out on the features that DOT language provides — namely, the styling attributes are often only honored if you’re rending with Graphviz. d3-graphviz will produce results very similar to Dagre but aligned more toward the DOT specification and style behaviors of Graphviz. However, it does not have feature parity with Graphviz, and statements/attributes that aren’t supported are simply ignored. Vizdom I believe it is a good compromise for the kinds of attributes programmatically generated DOT will consume. Vizdom Features 🚀 Blazing fast rendering 💾 Store graphs in links 0️⃣ Zero dependencies and no installation required Rendering Speed Well, it’s an apples-to-oranges comparison. Graphviz still produces visualizations and supports more , so I am subjectively comparing against Graphviz’s DOT engine, scoping it down to just DAGs, and even scoping it down to a list of narrow features of the DOT language. great layout engines Vizdom further Yes, I know it’s terrible to compare years of experience and tooling around the 30+ year streak that is Graphviz, but for my narrow use-case, it is crazy fast. Here are some wall times on my M1 Macbook Pro for rendering with ~400 nodes: a fairly large graph Native Graphviz: ~30 seconds Chrome (WASM) Graphviz: crashes* Chrome (Javascript) DagreJS: ~10 seconds Chrome (WASM) : Vizdom ~1 second** * It crashes because the emscripten flag, ALLOW_MEMORY_GROWTH=1 , is not set, therefore limiting the total memory to 16MB. This can be fixed, but I have not found an online project that can handle the graph in question. ** The example graph is rendered by selecting Example 14 in the editor view. Refreshing the page will result in you refreshing the page and getting a 414 because the URI that persists in the graph is too large. I am working on a better solution to store larger graphs. Store Graphs in Links You’ll notice when you make any changes to the DOT file, the URL immediately updates a few query parameters to store the graph and layout options, allowing you to always reference a copy of your data just by storing the link! There is a catch — large-ish graphs tend to make the URI too large for AWS (where is currently hosted) and other load balancers. Vizdom I’m currently working on a solution to handle larger graphs, but in the meantime, I’ve included a few ways to persist and store graphs: Copy an embeddable link that automatically renders a view that maximizes the graph. Copy an iframe snippet to embed in any application that supports rendering them. Or simply download the rendered SVG. Here’s an example of what the editor view looks like of the : pprof trace for deflate Go to the , and load Editor View Example 42 Diff View Occasionally, I found myself trying to compare two graphs that I’ve generated, so while I was at it, I added a to visually see changes between two different graphs. diff viewer Color Legend: Red: Deleted Orange: Modified Green: Added Here’s a few snaps: Click me: changing a node ID from “e” to “f” Click me: adding “cluster=true” Future Direction is still a work in progress, and as it continues to evolve, I am aware that the journey of perfecting this tool is as dynamic as the field of graph visualization itself. While I am proud of what has achieved so far, I am even more excited about its potential. That’s where you, the reader and potential user, come in. Vizdom Vizdom If you have any feedback, please feel free to message me over on or . Gitter Discord Thanks for reading — If you enjoyed this article, please ! follow [1]: E. R. Gansner, E. Koutsofios, S. C. North and K. . -P. Vo, “A technique for drawing directed graphs,” in IEEE Transactions on Software Engineering, vol. 19, no. 3, pp. 214–230, March 1993, doi: 10.1109/32.221135. Also published here

Here's Why You Shouldn't Use Graphviz for DAGs: What You Should Use Instead

এখানে আপনি কেন DAG-এর জন্য গ্রাফভিজ ব্যবহার করবেন না: পরিবর্তে আপনার কী ব্যবহার করা উচিত

Hier erfahren Sie, warum Sie Graphviz nicht für DAGs verwenden sollten: Was Sie stattdessen verwenden sollten

He aquí por qué no debería utilizar Graphviz para DAG: qué debería utilizar en su lugar

Voici pourquoi vous ne devriez pas utiliser Graphviz pour les DAG : ce que vous devriez utiliser à la place

यहां बताया गया है कि आपको डीएजी के लिए ग्राफ़विज़ का उपयोग क्यों नहीं करना चाहिए: इसके बजाय आपको क्या उपयोग करना चाहिए

DAG に Graphviz を使用すべきではない理由: 代わりに何を使用すべきか

DAG에 Graphviz를 사용하면 안되는 이유: 대신 사용해야 할 사항

Veja por que você não deve usar Graphviz para DAGs: o que você deve usar em vez disso

Вот почему вам не следует использовать Graphviz для DAG: что вам следует использовать вместо этого

İşte DAG'ler için Graphviz'i Neden Kullanmamalısınız: Bunun Yerine Ne Kullanmalısınız?

Đây là lý do tại sao bạn không nên sử dụng Graphviz cho DAG: Thay vào đó bạn nên sử dụng gì

这就是为什么你不应该将 Graphviz 用于 DAG：你应该使用什么

When talking about data processing, people often abbreviate it as “ETL.” However, if we look closely, data processing has undergone several iterations from ETL, ELT, XX ETL (such as Reverse ETL, Zero-ETL) to the currently popular EtLT architecture. While the Hadoop era mainly relied on ELT (Extract, Load, Transform) methods, the rise of real-time data warehouses and data lakes has rendered ELT obsolete. EtLT has emerged as the standard architecture for real-time data loading into data lakes and real-time data warehouses. Let’s explore the reasons behind the emergence of these architectures, their strengths and weaknesses, and why EtLT is gradually replacing ETL and ELT as the global mainstream data processing architecture, along with practical open-source methods. ETL Era (1990–2015) In the early days of data warehousing, Bill Inmmon, the proponent of data warehousing, defined it as a data storage architecture for partitioned subjects, where data was categorized and cleaned during storage. During this period, most data sources were structured databases (e.g., MySQL, Oracle, SQLServer, ERP, CRM), and data warehouses predominantly relied on OLTP databases (e.g., DB2, Oracle) for querying and historical storage. Handling complex ETL processes with such databases proved to be challenging. To address this, a plethora of ETL software emerged, such as Informatica, Talend, and Kettle, which greatly facilitated integrating complex data sources and offloading data warehouse workloads. Advantages: Clear technical architecture, smooth integration of complex data sources, and about 50% of the data warehouse work handled by ETL software. Disadvantages: All processing is done by data engineers, leading to longer fulfillment of business requirements; high hardware costs with double investments, especially when dealing with large data volumes. During the early and mid stages of data warehousing, when data source complexity was significantly higher, the ETL architecture became the industry standard and remained popular for over two decades. ELT Era (2005–2020) As data volumes grew, both data warehouse and ETL hardware costs escalated. New MPP (Massively Parallel Processing) and distributed technologies emerged, leading to the gradual shift from ETL to ELT architecture in the later stages of data warehousing and the rise of big data. Teradata, one of the major data warehouse vendors, and Hadoop Hive, a popular Hadoop-based data warehousing solution, adopted ELT architecture. They focused on direct loading (without complex transformation like join and group) of data into the data warehouse’s data staging layer, followed by further processing using SQL or H-SQL from the staging layer to the data atomic layer and finally to the summary layer and indicator layer. While Teradata targeted structured data and Hadoop targeted unstructured data, they adopted similar 3–4 layer data storage architectures and methodologies for data warehousing globally. Advantages: Utilizing data warehouse high-performance computing for large data volume processing, resulting in higher hardware ROI. Complex business logic can be handled by SQL, reducing the overall cost of data processing personnel by employing data analysts and business-savvy technical personnel, without the need to understand ETL tools like Spark and MapReduce. Disadvantages: Suitable for simple and large data volume scenarios. Inadequate for complex data sources and lacking support for real-time data warehousing requirements. ODS (Operational Data Store) was introduced as a transitional solution to handle complex data sources that ELT-based data warehouses couldn’t load and to improve real-time capabilities. ODS involved processing complex data sources through real-time CDC (Change Data Capture), real-time APIs, or short-batch processing (Micro-Batch) into a separate storage layer before ELT-ing them into the enterprise data warehouse. Currently, many enterprises still adopt this approach. Some companies place the Operational Data Store (ODS) within the data warehouse and use Spark and MapReduce for initial ETL (Extract, Transform, Load) processes. Later, they perform business data processing within the data warehouse (using tools like Hive, Teradata, Oracle, and DB2). At this stage, the early EtLT (Extract, Transform, Load, and Transform) community has already formed. It is characterized by a division of roles, where the complex processes of data extraction, Change Data Capture (CDC), data structuring, and standardization are often handled by data engineers, referred to as “t.” Their objective is to move data from the source system to the underlying data preparation layer or data atomic layer within the data warehouse. On the other hand, the processing of complex data atomic layers with business attributes, data aggregation, and generating data metrics (involving operations such as Group by and Join) is typically performed by business data engineers or data analysts who are skilled in using SQL. As a result of the emergence of the EtLT architecture, standalone projects like ODS have gradually faded out of the limelight due to the increase in data volume and the adoption of EtLT principles. EtLT Era (2020-Present) The EtLT architecture, as summarized by James Densmore in the , is a modern and globally popular data processing framework. EtLT emerged in response to the transformations in the modern data infrastructure. Data Pipelines Pocket Reference 2021 Background of the EtLT Architecture The modern data infrastructure has the following characteristics, which led to the emergence of the EtLT architecture: Cloud, SaaS, and Hybrid Local Data Sources Data Lake and Real-time Data Warehouses New Generation Big Data Federation Proliferation of AI Applications Fragmentation of Enterprise Data Community The appearance of complex data sources In the current global enterprise landscape, the advent of cloud and SaaS has made an already complex data source environment even more intricate. Dealing with SaaS data has given rise to a new concept of data ingestion, exemplified by tools like Fivetran and Airbyte, aiming to address the ELT (Extract, Load, Transform) challenges of ingesting SaaS data into data warehouses like Snowflake. Additionally, the complexity of data sources has increased with the proliferation of cloud-based data storage services (e.g., AWS Aurora, AWS RDS, MongoDB Service) and the coexistence of traditional on-premises databases and software (SAP, Oracle, DB2) in hybrid cloud architectures. Traditional ETL and ELT architectures are unable to cope with the intricacies of processing data in such a complex environment. Data Lake and Real-time Data Warehouses In modern data architecture, the emergence of data lakes has combined the features of traditional ODS (Operational Data Store) and data warehouses. Data lakes enable real-time data processing and facilitate data changes at the source (e.g., Apache Hudi, Apache Iceberg, Databricks Delta Lake). Simultaneously, the concept of real-time data warehouses has surfaced, with various new computing engines (e.g., Apache Pinot, ClickHouse, Apache Doris) making real-time ETL a priority. However, traditional CDC ETL tools or real-time stream processing platforms face challenges in providing adequate support for data lakes and real-time data warehouses, either due to compatibility issues with new storage engines or limitations in connecting to modern data sources, lacking robust architecture and tool support. The Emergence of a New Generation of Big Data Federation In modern data architecture, a new breed of architectures has emerged, aiming to minimize data movement across different data stores and enabling complex queries directly through connectors or rapid data loading. Examples include Starburst’s TrinoDB (formerly PrestoDB) and OneHouse based on Apache Hudi. These tools excel at data caching and on-the-fly cross-data-source queries, making them unsuitable for support by traditional ETL/ELT tools in this new Big Data Federation paradigm. The Rise of Large-scale Models With the emergence of ChatGPT in 2022, AI models have become algorithmically feasible for widespread enterprise applications. The bottleneck for AI application deployment now lies in data supply, which has been addressed by data lakes and Big Data Federation for data storage and querying. However, traditional ETL, ELT, and stream processing have become bottlenecks for data supply, either unable to quickly integrate various complex traditional and emerging data sources or failing to support diverse data requirements for both AI training and online AI applications using a single codebase. Fragmentation of the Enterprise Data Community As data-driven approaches become more ingrained in enterprises, the number of data users within organizations has rapidly increased. These users range from traditional data engineers to data analysts, AI practitioners, sales analysts, and financial analysts, each with diverse data requirements. After experiencing various shifts like No-SQL and New-SQL, SQL has emerged as the sole standard for complex business analysis. A considerable number of analysts and business unit engineers now use SQL to address the “last mile” problem of data analysis within enterprises. Meanwhile, the handling of complex unstructured data is left to professional data engineers using technologies like Spark, MapReduce, and Flink. Consequently, the demands of these two groups diverge significantly, making traditional ETL and ELT architectures inadequate to meet the needs of modern enterprise users. EtLT Architecture Emerges! Against the backdrop mentioned above, data processing has gradually evolved into the EtLT architecture: EtLT Architecture Overview: EtLT splits the traditional ETL and ELT structures and combines real-time and batch processing to accommodate real-time data warehouses and AI application requirements. E(xtract) — Extraction: EtLT supports traditional on-premises databases, files, and software as well as emerging cloud databases, SaaS software APIs, and serverless data sources. It can perform real-time CDC on database binlog and real-time stream processing (e.g., Kafka Streaming) and also handle bulk data reading (multi-threaded partition reading, rate limiting, etc.). t(ransform) — Normalization: In addition to ETL and ELT, EtLT introduces a small “t,” focusing on data normalization. It rapidly converts complex and heterogeneous extracted data sources into structured data that can be readily loaded into the target data storage. It deals with real-time CDC by splitting, filtering, and changing field formats, supporting both batch and real-time distribution to the final Load stage. L(oad) — Loading: The loading stage is no longer just about data loading but also involves adapting data source structures and content to suit the target data destination (Data Target). It should handle data structure changes (Schema Evolution) in the source and support efficient loading methods such as Bulk Load, SaaS loading (Reverse ETL), and JDBC loading. EtLT ensures support for real-time data and data structure changes, along with fast batch data loading. (T)ransform — Conversion: In cloud data warehouses, on-premises data warehouses, or new data federations, business logic is processed. This is typically achieved using SQL, either in real-time or batch mode, to transform complex business rules accurately and quickly into data usable by business or AI applications. In the EtLT architecture, different user roles have distinct responsibilities: EtL Phase: Primarily handled by data engineers who convert complex and heterogeneous data sources into data that can be loaded into the data warehouse or data federation. They do not need to have an in-depth understanding of enterprise metric calculation rules but must be proficient in transforming various source and unstructured data into structured data. Their focus is on data timeliness and the accuracy of transforming source data into structured data. T Phase: Led by data analysts, business SQL developers, and AI engineers who possess a deep understanding of enterprise business rules. They convert business rules into SQL statements to perform analysis and statistics on the underlying structured data, ultimately achieving data analysis within the enterprise and enabling AI applications. Their focus is on data logic relationships, data quality, and meeting business requirements for final data results. Open-Source Implementation of EtLT There are several open-source implementations of EtLT in modern data architecture. Examples include DBT, which helps analysts and business developers quickly develop data applications based on Snowflake, and Apache DolphinScheduler, a visual workflow orchestration tool for big data tasks. DolphinScheduler plans to introduce a Task IDE, allowing data analysts to directly debug SQL tasks for Hudi, Hive, Presto, ClickHouse, and more, and create workflow tasks through drag-and-drop. As a representative of the EtLT architecture, Apache SeaTunnel started with support for various cloud and on-premises data sources, gradually expanding its capabilities to include SaaS and Reverse ETL, as well as accommodating the demands of large-scale model data supply. It has been continually refining the EtLT landscape. The latest SeaTunnel Zeta computing engine delegates complex operations such as Join and Groupby to the ultimate data warehouse endpoint, focusing on data normalization and standardization. This approach aims to achieve the goal of unified real-time and batch data processing with a single set of code and a high-performance engine. Additionally, SeaTunnel now includes support for large-scale models, making it possible for these models to directly interact with over 100 supported data sources (refer to ), ranging from traditional databases to cloud databases and ultimately SaaS. https://seatunnel.apache.org/docs/Connector-v2-release-state/ Since joining the Apache Incubator in late 2022, Apache SeaTunnel has witnessed a five-fold growth in one year, and currently, it supports more than 100 data sources (as seen at ). The connector support has been progressively improved, encompassing traditional databases, cloud databases, and SaaS offerings. https://seatunnel.apache.org/docs/2.3.2/category/source-v2 The release of SeaTunnel Zeta Engine in Apache SeaTunnel 2.3.0 brings features such as data distributed CDC, schema evolution for target source data tables, and the synchronization of entire databases and multiple tables. Its excellent performance has garnered attention from numerous global users, including Bharti Airtel, the second-largest telecommunications operator in India, Shopee.com, an e-commerce platform in Singapore, and Vip.com, a major online retailer. Large Scale Model Training support One noteworthy aspect is that SeaTunnel now offers support for large-scale model training and vector databases, enabling seamless interactions between large models and the 100+ supported data sources of SeaTunnel (see the article ). Furthermore, SeaTunnel can leverage ChatGPT to directly generate SaaS Connectors, facilitating rapid access to a wide range of internet information for your large-scale models and data warehouses. Breakthrough in the book search field! Use Apache SeaTunnel, Milvus, and OpenAI to improve the accuracy and efficiency of book title similarity search As the complexity of AI, cloud, and SaaS continues to increase, the demand for real-time CDC, SaaS, data lakes, and real-time data warehouse loading has made simple ETL architectures inadequate to meet the needs of modern enterprises. EtLT architecture, tailored for different stages of enterprise development, is destined to shine in the modern data infrastructure. With the mission of “Connecting the world’s data sources and synchronizing them as swiftly as flying”, Apache SeaTunnel warrants the attention of all data professionals. Also published . here

ELT is Dead, and EtLT Will End Modern Data Processing Architecture

At length Louis XIV, bowed beneath the weight of a reign of sixty years

Nimes: A Crucible of Convulsions - From Religious Rivalry to Martyrdom

The Murder on the Links by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . M. Giraud Finds Some Clues IX. M. Giraud Finds Some Clues In the Salon I found the examining magistrate busily interrogating the old gardener Auguste. Poirot and the commissary, who were both present, greeted me respectively with a smile and a polite bow. I slipped quietly into a seat. M. Hautet was painstaking and meticulous in the extreme, but did not succeed in eliciting anything of importance. The gardening gloves Auguste admitted to be his. He wore them when handling a certain species of primula plant which was poisonous to some people. He could not say when he had worn them last. Certainly he had not missed them. Where were they kept? Sometimes in one place, sometimes in another. The spade was usually to be found in the small tool shed. Was it locked? Of course it was locked. Where was the key kept? Parbleu, it was in the door of course! There was nothing of value to steal. Who would have expected a party of bandits, of assassins? Such things did not happen in Madame la Vicomtesse’s time. M. Hautet signifying that he had finished with him, the old man withdrew, grumbling to the last. Remembering Poirot’s unaccountable insistence on the footprints in the flower beds, I scrutinized him narrowly as he gave his evidence. Either he had nothing to do with the crime or he was a consummate actor. Suddenly, just as he was going out of the door, an idea struck me. “Pardon M. Hautet,” I cried, “but will you permit me to ask him one question?” “But certainly, monsieur.” Thus encouraged, I turned to Auguste. “Where do you keep your boots?” “Sac à papier!” growled the old man. “On my feet. Where else?” “But when you go to bed at night?” “Under my bed.” “But who cleans them?” “Nobody. Why should they be cleaned? Is it that I promenade myself on the front like a young man? On Sunday I wear the Sunday boots, bien entendu, but otherwise—!” he shrugged his shoulders. I shook my head, discouraged. “Well, well,” said the magistrate. “We do not advance very much. Undoubtedly we are held up until we get the return cable from Santiago. Has any one seen Giraud? In verity that one lacks politeness! I have a very good mind to send for him and—” “You will not have to send far, M. le juge.” The quiet voice startled us. Giraud was standing outside looking in through the open window. He leaped lightly into the room, and advanced to the table. “Here I am, M. le juge, at your service. Accept my excuses for not presenting myself sooner.” “Not at all. Not at all,” said the magistrate, rather confused. “Of course I am only a detective,” continued the other. “I know nothing of interrogatories. Were I conducting one, I should be inclined to do so without an open window. Any one standing outside can so easily hear all that passes. … But no matter.” M. Hautet flushed angrily. There was evidently going to be no love lost between the examining magistrate and the detective in charge of the case. They had fallen foul of each other at the start. Perhaps in any event it would have been much the same. To Giraud, all examining magistrates were fools, and to M. Hautet who took himself seriously, the casual manner of the Paris detective could not fail to give offence. “Eh bien, M. Giraud,” said the magistrate rather sharply. “Without doubt you have been employing your time to a marvel? You have the names of the assassins for us, have you not? And also the precise spot where they find themselves now?” Unmoved by this irony, Giraud replied: “I know at least where they have come from.” “Comment?” Giraud took two small objects from his pocket and laid them down on the table. We crowded round. The objects were very simple ones: the stub of a cigarette, and an unlighted match. The detective wheeled round on Poirot. “What do you see there?” he asked. There was something almost brutal in his tone. It made my cheeks flush. But Poirot remained unmoved. He shrugged his shoulders. “A cigarette end, and a match.” “And what does that tell you?” Poirot spread out his hands. “It tells me—nothing.” “Ah!” said Giraud, in a satisfied voice. “You haven’t made a study of these things. That’s not an ordinary match—not in this country at least. It’s common enough in South America. Luckily it’s unlighted. I mightn’t have recognized it otherwise. Evidently one of the men threw away his cigarette end, and lit another, spilling one match out of the box as he did so.” “And the other match?” asked Poirot. “Which match?” “The one he did light his cigarette with. You have found that also?” “No.” “Perhaps you didn’t search very thoroughly.” “Not search thoroughly—” For a moment it seemed as though the detective were going to break out angrily, but with an effort he controlled himself. “I see you love a joke, M. Poirot. But in any case, match or no match, the cigarette end would be sufficient. It is a South American cigarette with liquorice pectoral paper.” Poirot bowed. The commissary spoke: “The cigarette end and match might have belonged to M. Renauld. Remember, it is only two years since he returned from South America.” “No,” replied the other confidently. “I have already searched among the effects of M. Renauld. The cigarettes he smoked and the matches he used are quite different.” “You do not think it odd,” asked Poirot, “that these strangers should come unprovided with a weapon, with gloves, with a spade, and that they should so conveniently find all these things?” Giraud smiled in a rather superior manner. “Undoubtedly it is strange. Indeed, without the theory that I hold, it would be inexplicable.” “Aha!” said M. Hautet. “An accomplice. An accomplice within the house!” “Or outside it,” said Giraud with a peculiar smile. “But some one must have admitted them? We cannot allow that, by an unparalleled piece of good fortune, they found the door ajar for them to walk in?” “D’accord, M. le juge. The door was opened for them, but it could just as easily be opened from outside—by some one who possessed a key.” “But who did possess a key?” Giraud shrugged his shoulders. “As for that, no one who possesses one is going to admit the fact if they can help it. But several people might have had one. M. Jack Renauld, the son, for instance. It is true that he is on his way to South America, but he might have lost the key or had it stolen from him. Then there is the gardener—he has been here many years. One of the younger servants may have a lover. It is easy to take an impression of a key and have one cut. There are many possibilities. Then there is another person who, I should judge, is exceedingly likely to have such a thing in her keeping.” “Who is that?” “Madame Daubreuil,” said the detective dryly. “Eh, eh!” said the magistrate, his face falling a little, “so you have heard about that, have you?” “I hear everything,” said Giraud imperturbably. “There is one thing I could swear you have not heard,” said M. Hautet, delighted to be able to show superior knowledge, and without more ado, he retailed the story of the mysterious visitor the night before. He also touched on the cheque made out to “Duveen,” and finally handed Giraud the letter signed “Bella.” Giraud listened in silence, studied the letter attentively, and then handed it back. “All very interesting, M. le juge. But my theory remains unaffected.” “And your theory is?” “For the moment I prefer not to say. Remember, I am only just beginning my investigations.” “Tell me one thing, M. Giraud,” said Poirot suddenly. “Your theory allows for the door being opened. It does not explain why it was left open. When they departed, would it not have been natural for them to close it behind them. If a sergent de ville had chanced to come up to the house, as is sometimes done to see that all is well, they might have been discovered and overtaken almost at once.” “Bah! They forgot it. A mistake, I grant you.” Then, to my surprise, Poirot uttered almost the same words as he had uttered to Bex the previous evening: “I do not agree with you. The door being left open was the result of either design or necessity, and any theory that does not admit that fact is bound to prove vain.” We all regarded the little man with a good deal of astonishment. The confession of ignorance drawn from him over the match end had, I thought, been bound to humiliate him, but here he was self satisfied as ever, laying down the law to the great Giraud without a tremor. The detective twisted his moustache, eyeing my friend in a somewhat bantering fashion. “You don’t agree with me, eh? Well, what strikes you particularly about the case. Let’s hear your views.” “One thing presents itself to me as being significant. Tell me, M. Giraud, does nothing strike you as familiar about this case? Is there nothing it reminds you of?” “Familiar? Reminds me of? I can’t say off-hand. I don’t think so, though.” “You are wrong,” said Poirot quietly. “A crime almost precisely similar has been committed before.” “When? And where?” “Ah, that, unfortunately, I cannot for the moment remember—but I shall do so. I had hoped you might be able to assist me.” Giraud snorted incredulously. “There have been many affairs of masked men! I cannot remember the details of them all. These crimes all resemble each other more or less.” “There is such a thing as the individual touch.” Poirot suddenly assumed his lecturing manner, and addressed us collectively. “I am speaking to you now of the psychology of crime. M. Giraud knows quite well that each criminal has his particular method, and that the police, when called in to investigate—say a case of burglary—can often make a shrewd guess at the offender, simply by the peculiar method he has employed. (Japp would tell you the same, Hastings.) Man is an unoriginal animal. Unoriginal within the law in his daily respectable life, equally unoriginal outside the law. If a man commits a crime, any other crime he commits will resemble it closely. The English murderer who disposed of his wives in succession by drowning them in their baths was a case in point. Had he varied his methods, he might have escaped detection to this day. But he obeyed the common dictates of human nature, arguing that what had once succeeded would succeed again, and he paid the penalty of his lack of originality.” “And the point of all this?” sneered Giraud. “That when you have two crimes precisely similar in design and execution, you find the same brain behind them both. I am looking for that brain, M. Giraud—and I shall find it. Here we have a true clue—a psychological clue. You may know all about cigarettes and match ends, M. Giraud, but I, Hercule Poirot, know the mind of man!” And the ridiculous little fellow tapped his forehead with emphasis. Giraud remained singularly unimpressed. “For your guidance,” continued Poirot, “I will also advise you of one fact which might fail to be brought to your notice. The wrist watch of Madame Renauld, on the day following the tragedy, had gained two hours. It might interest you to examine it.” Giraud stared. “Perhaps it was in the habit of gaining?” “As a matter of fact, I am told it did.” “Eh bien, then!” “All the same, two hours is a good deal,” said Poirot softly. “Then there is the matter of the footprints in the flower-bed.” He nodded his head towards the open window. Giraud took two eager strides, and looked out. “This bed here?” “Yes.” “But I see no footprints?” “No,” said Poirot, straightening a little pile of books on a table. “There are none.” For a moment an almost murderous rage obscured Giraud’s face. He took two strides towards his tormentor, but at that moment the salon door was opened, and Marchaud announced. “M. Stonor, the secretary, has just arrived from England. May he enter?” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (2019). The Murder on the Links. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/58866/pg58866-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

M. Giraud Finds Some Clues

The Murder on the Links by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . At the Villa Geneviève III. At the Villa Geneviève In a moment Poirot had leapt from the car, his eyes blazing with excitement. He caught the man by the shoulder. “What is that you say? Murdered? When? How?” The sergent de ville drew himself up. “I cannot answer any questions, monsieur.” “True. I comprehend.” Poirot reflected for a minute. “The Commissary of Police, he is without doubt within?” “Yes, monsieur.” Poirot took out a card, and scribbled a few words on it. “Voilà! Will you have the goodness to see that this card is sent in to the commissary at once?” The man took it and, turning his head over his shoulder, whistled. In a few seconds a comrade joined him and was handed Poirot’s message. There was a wait of some minutes, and then a short stout man with a huge moustache came bustling down to the gate. The sergent de ville saluted and stood aside. “My dear M. Poirot,” cried the new-comer, “I am delighted to see you. Your arrival is most opportune.” Poirot’s face had lighted up. “M. Bex! This is indeed a pleasure.” He turned to me. “This is an English friend of mine, Captain Hastings—M. Lucien Bex.” The commissary and I bowed to each other ceremoniously, then M. Bex turned once more to Poirot. “Mon vieux, I have not seen you since 1909, that time in Ostend. I heard that you had left the Force?” “So I have. I run a private business in London.” “And you say you have information to give which may assist us?” “Possibly you know it already. You were aware that I had been sent for?” “No. By whom?” “The dead man. It seems he knew an attempt was going to be made on his life. Unfortunately he sent for me too late.” “Sacri tonnerre!” ejaculated the Frenchman. “So he foresaw his own murder? That upsets our theories considerably! But come inside.” He held the gate open, and we commenced walking towards the house. M. Bex continued to talk: “The examining magistrate, M. Hautet, must hear of this at once. He has just finished examining the scene of the crime and is about to begin his interrogations. A charming man. You will like him. Most sympathetic. Original in his methods, but an excellent judge.” “When was the crime committed?” asked Poirot. “The body was discovered this morning about nine o’clock. Madame Renauld’s evidence, and that of the doctors goes to show that the death must have occurred about 2 a.m. But enter, I pray of you.” We had arrived at the steps which led up to the front door of the Villa. In the hall another sergent de ville was sitting. He rose at sight of the commissary. “Where is M. Hautet now?” inquired the latter. “In the salon, monsieur.” M. Bex opened a door to the left of the hall, and we passed in. M. Hautet and his clerk were sitting at a big round table. They looked up as we entered. The commissary introduced us, and explained our presence. M. Hautet, the Juge d’Instruction, was a tall, gaunt man, with piercing dark eyes, and a neatly cut grey beard, which he had a habit of caressing as he talked. Standing by the mantelpiece was an elderly man, with slightly stooping shoulders, who was introduced to us as Dr. Durand. “Most extraordinary,” remarked M. Hautet, as the commissary finished speaking. “You have the letter here, monsieur?” Poirot handed it to him, and the magistrate read it. “H’m. He speaks of a secret. What a pity he was not more explicit. We are much indebted to you, M. Poirot. I hope you will do us the honour of assisting us in our investigations. Or are you obliged to return to London?” “M. le juge, I propose to remain. I did not arrive in time to prevent my client’s death, but I feel myself bound in honour to discover the assassin.” The magistrate bowed. “These sentiments do you honour. Also, without doubt, Madame Renauld will wish to retain your services. We are expecting M. Giraud from the Sûreté in Paris any moment, and I am sure that you and he will be able to give each other mutual assistance in your investigations. In the meantime, I hope that you will do me the honour to be present at my interrogations, and I need hardly say that if there is any assistance you require it is at your disposal.” “I thank you, monsieur. You will comprehend that at present I am completely in the dark. I know nothing whatever.” M. Hautet nodded to the commissary, and the latter took up the tale: “This morning, the old servant Françoise, on descending to start her work, found the front door ajar. Feeling a momentary alarm as to burglars, she looked into the dining-room, but seeing the silver was safe she thought no more about it, concluding that her master had, without doubt, risen early, and gone for a stroll.” “Pardon, monsieur, for interrupting, but was that a common practice of his?” “No, it was not, but old Françoise has the common idea as regards the English—that they are mad, and liable to do the most unaccountable things at any moment! Going to call her mistress as usual, a younger maid, Léonie, was horrified to discover her gagged and bound, and almost at the same moment news was brought that M. Renauld’s body had been discovered, stone dead, stabbed in the back.” “Where?” “That is one of the most extraordinary features of the case. M. Poirot, the body was lying, face downwards, in an open grave.” “What?” “Yes. The pit was freshly dug—just a few yards outside the boundary of the Villa grounds.” “And he had been dead—how long?” Dr. Durand answered this. “I examined the body this morning at ten o’clock. Death must have taken place at least seven, and possibly ten hours previously.” “H’m, that fixes it at between midnight and 3 a.m.” “Exactly, and Madame Renauld’s evidence places it at after 2 a.m. which narrows the field still further. Death must have been instantaneous, and naturally could not have been self-inflicted.” Poirot nodded, and the commissary resumed: “Madame Renauld was hastily freed from the cords that bound her by the horrified servants. She was in a terrible condition of weakness, almost unconscious from the pain of her bonds. It appears that two masked men entered the bedroom, gagged and bound her, whilst forcibly abducting her husband. This we know at second hand from the servants. On hearing the tragic news, she fell at once into an alarming state of agitation. On arrival, Dr. Durand immediately prescribed a sedative, and we have not yet been able to question her. But without doubt she will awake more calm, and be equal to bearing the strain of the interrogation.” The commissary paused. “And the inmates of the house, monsieur?” “There is old Françoise, the housekeeper, she lived for many years with the former owners of the Villa Geneviève. Then there are two young girls, sisters, Denise and Léonie Oulard. Their home is in Merlinville, and they come of the most respectable parents. Then there is the chauffeur whom M. Renauld brought over from England with him, but he is away on a holiday. Finally there are Madame Renauld and her son, M. Jack Renauld. He, too, is away from home at present.” Poirot bowed his head. M. Hautet spoke: “Marchaud!” The sergent de ville appeared. “Bring in the woman Françoise.” The man saluted, and disappeared. In a moment or two, he returned, escorting the frightened Françoise. “You name is Françoise Arrichet?” “Yes, monsieur.” “You have been a long time in service at the Villa Geneviève?” “Eleven years with Madame la Vicomtesse. Then when she sold the Villa this spring, I consented to remain on with the English milor. Never did I imagine—” The magistrate cut her short. “Without doubt, without doubt. Now, Françoise, in this matter of the front door, whose business was it to fasten it at night?” “Mine, monsieur. Always I saw to it myself.” “And last night?” “I fastened it as usual.” “You are sure of that?” “I swear it by the blessed saints, monsieur.” “What time would that be?” “The same time as usual, half-past ten, monsieur.” “What about the rest of the household, had they gone up to bed?” “Madame had retired some time before. Denise and Léonie went up with me. Monsieur was still in his study.” “Then, if any one unfastened the door afterwards, it must have been M. Renauld himself?” Françoise shrugged her broad shoulders. “What should he do that for? With robbers and assassins passing every minute! A nice idea! Monsieur was not an imbecile. It is not as though he had had to let cette dame out—” The magistrate interrupted sharply: “Cette dame? What lady do you mean?” “Why, the lady who came to see him.” “Had a lady been to see him that evening?” “But yes, monsieur—and many other evenings as well.” “Who was she? Did you know her?” A rather cunning look spread over the woman’s face. “How should I know who it was?” she grumbled. “I did not let her in last night.” “Aha!” roared the examining magistrate, bringing his hand down with a bang on the table. “You would trifle with the police, would you? I demand that you tell me at once the name of this woman who came to visit M. Renauld in the evenings.” “The police—the police,” grumbled Françoise. “Never did I think that I should be mixed up with the police. But I know well enough who she was. It was Madame Daubreuil.” The commissary uttered an exclamation, and leaned forward as though in utter astonishment. “Madame Daubreuil—from the Villa Marguerite just down the road?” “That is what I said, monsieur. Oh, she is a pretty one, cellela!” The old woman tossed her head scornfully. “Madame Daubreuil,” murmured the commissary. “Impossible.” “Voilà,” grumbled Françoise. “That is all you get for telling the truth.” “Not at all,” said the examining magistrate soothingly. “We were surprised, that is all. Madame Daubreuil then, and Monsieur Renauld, they were—” he paused delicately. “Eh? It was that without doubt?” “How should I know? But what will you? Monsieur, he was milor anglais—trés riche—and Madame Daubreuil, she was poor, that one—and trés chic for all that she lives so quietly with her daughter. Not a doubt of it, she has had her history! She is no longer young, but ma foi! I who speak to you have seen the men’s heads turn after her as she goes down the street. Besides lately, she has had more money to spend—all the town knows it. The little economies, they are at an end.” And Françoise shook her head with an air of unalterable certainty. M. Hautet stroked his beard reflectively. “And Madame Renauld?” he asked at length. “How did she take this—friendship.” Françoise shrugged her shoulders. “She was always most amiable—most polite. One would say that she suspected nothing. But all the same, is it not so, the heart suffers, monsieur? Day by day, I have watched Madame grow paler and thinner. She was not the same woman who arrived here a month ago. Monsieur, too, has changed. He also has had his worries. One could see that he was on the brink of a crisis of the nerves. And who could wonder, with an affair conducted such a fashion? No reticence, no discretion. Style anglais, without doubt!” I bounded indignantly in my seat, but the examining magistrate was continuing his questions, undistracted by side issues. “You say that M. Renauld had not to let Madame Daubreuil out? Had she left, then?” “Yes, monsieur. I heard them come out of the study and go to the door. Monsieur said good night, and shut the door after her.” “What time was that?” “About twenty-five minutes after ten, monsieur.” “Do you know when M. Renauld went to bed?” “I heard him come up about ten minutes after we did. The stair creaks so that one hears every one who goes up and down.” “And that is all? You heard no sound of disturbance during the night?” “Nothing whatever, monsieur.” “Which of the servants came down the first in the morning?” “I did, monsieur. At once I saw the door swinging open.” “What about the other downstairs windows, were they all fastened?” “Every one of them. There was nothing suspicious or out of place anywhere.” “Good, Françoise, you can go.” The old woman shuffled towards the door. On the threshold she looked back. “I will tell you one thing, monsieur. That Madame Daubreuil she is a bad one! Oh, yes, one woman knows about another. She is a bad one, remember that.” And, shaking her head sagely, Françoise left the room. “Léonie Oulard,” called the magistrate. Léonie appeared dissolved in tears, and inclined to be hysterical. M. Hautet dealt with her adroitly. Her evidence was mainly concerned with the discovery of her mistress gagged and bound, of which she gave rather an exaggerated account. She, like Françoise, had heard nothing during the night. Her sister, Denise, succeeded her. She agreed that her master had changed greatly of late. “Every day he became more and more morose. He ate less. He was always depressed.” But Denise had her own theory. “Without doubt it was the Mafia he had on his track! Two masked men—who else could it be? A terrible society that!” “It is, of course, possible,” said the magistrate smoothly. “Now, my girl, was it you who admitted Madame Daubreuil to the house last night?” “Not last night, monsieur, the night before.” “But Françoise has just told us that Madame Daubreuil was here last night?” “No, monsieur. A lady did come to see M. Renauld last night, but it was not Madame Daubreuil.” Surprised, the magistrate insisted, but the girl held firm. She knew Madame Daubreuil perfectly by sight. This lady was dark also, but shorter, and much younger. Nothing could shake her statement. “Had you ever seen this lady before?” “Never, monsieur.” And then the girl added diffidently: “But I think she was English.” “English?” “Yes, monsieur. She asked for M. Renauld in quite good French, but the accent—one can always tell it, n’est-ce pas? Besides when they came out of the study they were speaking in English.” “Did you hear what they said? Could you understand it, I mean?” “Me, I speak the English very well,” said Denise with pride. “The lady was speaking too fast for me to catch what she said, but I heard Monsieur’s last words as he opened the door for her.” She paused, and then repeated carefully and laboriously: “ ‘Yeas—yeas—butt for Gaud’s saike go nauw!’ ” “Yes, yes, but for God’s sake go now!” repeated the magistrate. He dismissed Denise and, after a moment or two for consideration, recalled Françoise. To her he propounded the question as to whether she had not made a mistake in fixing the night of Madame Daubreuil’s visit. Françoise, however, proved unexpectedly obstinate. It was last night that Madame Daubreuil had come. Without a doubt it was she. Denise wished to make herself interesting, voilà tout! So she had cooked up this fine tale about a strange lady. Airing her knowledge of English too! Probably Monsieur had never spoken that sentence in English at all, and even if he had, it proved nothing, for Madame Daubreuil spoke English perfectly, and generally used that language when talking to M. and Madame Renauld. “You see, M. Jack, the son of Monsieur, was usually here, and he spoke the French very badly.” The magistrate did not insist. Instead he inquired about the chauffeur, and learned that only yesterday, M. Renauld had declared that he was not likely to use the car, and that Masters might just as well take a holiday. A perplexed frown was beginning to gather between Poirot’s eyes. “What is it?” I whispered. He shook his head impatiently, and asked a question: “Pardon, M. Bex, but without doubt M. Renauld could drive the car himself?” The commissary looked over at Françoise, and the old woman replied promptly: “No, Monsieur did not drive himself.” Poirot’s frown deepened. “I wish you would tell me what is worrying you,” I said impatiently. “See you not? In his letter M. Renauld speaks of sending the car for me to Calais.” “Perhaps he meant a hired car,” I suggested. “Doubtless that is so. But why hire a car when you have one of your own. Why choose yesterday to send away the chauffeur on a holiday—suddenly, at a moment’s notice? Was it that for some reason he wanted him out of the way before we arrived?” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (2019). The Murder on the Links. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/58866/pg58866-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

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VIZCOM

vizcom.ai

1-10 employees

Since 2021

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VIZCOM

EVERGREEN INDEX #11358

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