API Integration Challenges

There’s a truth in our industry that nobody wants to say out loud: We don’t have a product problem. We have an integration problem. I sat through a presentation recently by Steve Greenfield, where he put up a slide listing every vendor touching the dealership ecosystem. 500 companies. I looked at that board and thought, “I’m paying every one of them.” That’s the problem. Every new “solution” creates a new silo. Every silo creates friction. And every bit of friction lands directly on the customer’s experience and on the backs of our teams. We’ve let complexity grow faster than value. And here’s the part that should make every dealer stop in their tracks: These vendors charge us tolls to use our own data. For our information. For our customer relationships. The PII, the personal identifiable information is the key to everything: Better service. Faster transactions. Cleaner processes. Stronger retention. And we’ve given that key away too easily. Not anymore. At Paragon, we’re stripping the system back to what actually matters: • Fewer partners • Zero vendors • Full integration • No duplication • No friction • No tolls on our data • A single ecosystem built for speed, clarity, and customer experience I watched a team member switch through ten different windows just to complete a simple transaction. Ten windows. One customer. One task. It’s absurd. It’s not innovation. It’s not efficiency. It’s not progress. It’s what happens when you build a business around tools instead of building tools around a business. The future isn’t more software. It’s smarter integration. It’s aligning human intelligence with machine intelligence so the customer gets the fastest, cleanest, most seamless experience possible. Because at the end of the day, that’s our job: Protect the customer. Protect the team. And remove anything that gets in the way of both. This is operational survival. And the dealerships that understand that first will be the ones standing tallest tomorrow.

Today, we are releasing the Data Contract Specification on https://datacontract.com. Data Contracts bring data provider and data consumer teams together. First, and foremost, data contracts are a communication tool to express a common understanding of how data should be structured and interpreted. They make semantic and quality expectations explicit. Later in development and production, they serve as a formal interface specification to generate code, automate schema validations, quality checks, and much more. We are very excited about the recent discussions and positive developments around data contracts, thanks to Andrew Jones, Jean-Georges Perrin, Chad Sanderson, and Scott Hirleman (just to name a few). We want to contribute and propose the Data Contract Specification as a common format that is based on OpenAPI and AsyncAPI conventions. The data contract specification defines a YAML format to describe attributes of provided data sets. It is data platform neutral, yet supports well-known formats to for schemas (e.g., dbt models, JSON Schema, Protobuf, SQL DDL) and quality tests (e.g., SodaCL, Great Expectations, SQL queries) to avoid unnecessary abstractions. Find an example and the full specification on datacontract.com. The data contract specification is an open initiative, and we welcome everyone to contribute on https://lnkd.in/ezisAFsV. Is anything missing? We would love to hear your feedback and thoughts.

I wish more software engineers recognized Hyrum's Law. "With a sufficient number of users of an API, it does not matter what you promise in the contract: all observable behaviors of your system will be depended upon by somebody." This is why breaking changes are so difficult in popular systems, even when they shouldn't be according to documented APIs. Some related examples include: - Internal APIs that can't be changed because teams rely on undocumented side effects - Database schemas that become immutable due to discovered downstream dependencies of other services that call the database other than the owning system, like data engineering teams - UI elements that can't be modified because users have built workflows around specific behaviors - Configuration options that become permanent because automation tools depend on them Documentation and intentions matter less than actual behavior. Successful API design requires thinking beyond the documented contract to consider all observable behaviors. And why backward compatibility often means preserving behaviors you never intended to expose in the first place.

I’ve been fortunate to lead our global AWS capability at NTT DATA, and I’m genuinely energized by what agentic AI is unlocking for clients right now. When agents can perceive, reason, and act across AWS-native services and enterprise systems, the conversation finally shifts from “What can AI generate?” to “What measurable business outcomes can an agent deliver?” Across engagements in North America, EMEA, and APAC, my team and I consistently see six recurring pitfalls that delay or derail agentic AI adoption. Here’s what to avoid, and what to do instead: 1. Lack of GenAI skills at scale Do instead: Create a structured enablement engine—hands-on labs, AWS GenAI jump-start programs(start using AWS strands framework and KIRO IDE), playbooks, and a clear CoE model. Build talent through pairing delivery teams with seasoned architects so prompting, evaluation, and guardrails, setting up MCP and Agent Orchestration are learned by doing. Don’t chase the most expensive AI specialist on the market. You can’t scale that. 2. Cost overruns from weak planning & budgeting Do instead: Establish FinOps guardrails early (budgets, alerts, quotas). Simulate workloads and enforce usage policies so agents don’t “run wild.” Tie every experiment to business value with clear stage gates. And always ask the team: Can this be done more efficiently using a custom SLM on AWS? 3. Technology debt buried in legacy estates Do instead: Build a modernization roadmap. Containerize, decouple, favor event-driven patterns, and leverage AWS managed services to reduce the operational drag on agents. If you haven’t explored AWS Transform, you should. 4. Haphazard data management & agent evolution Do instead: Create a unified data foundation with clear contracts and lineage. Implement MLOps/AIOps for continuous evaluation, retraining, and safe rollout of agent updates. 5. Integration complexity & compatibility issues Do instead: Standardize on API-first design, shared schemas, and event buses. Use integration sandboxes and test harnesses so agents interact reliably with existing applications. 6. Governance, security & compliance gaps Do instead: Apply secure-by-design principles from day one—RBAC, encryption, auditability, human-in-the-loop, and well-maintained risk registers for agent behaviors. If you’re exploring agentic AI on AWS, or you’re ready to scale pilots into production, let’s connect at AWS re:Invent (Dec 1–5). I’d love to compare notes, share patterns that work, and trade ideas on what’s next. #AWS #AgenticAI #NTTDATA #reInvent #GenAI #AIatScale Ryan Reed Ayman Husain Jose Kuzhivelil Charlie Doubek Clive Charlton Abhishek Lakhani Dana Schmidt Sean McCarron

If your JavaScript codebase is trash, converting it to TypeScript will not clean it up. It will simply give your trash, better error messages. Backward compatibility is where you learn good engineering.  And most people learn it painfully. You see, everyone is excited about: – New tooling – New frameworks – New benchmarks – New language versions Nobody is excited about: – Old data formats – Old production traffic – Old mobile app versions – Old third-party integrations – Old customers who never update – Old promises your API made five years ago That carrot tweet is funny because it captures the lie we tell ourselves. In cooking, tools do not randomly break carrots. In software, your tools break users for fun. You upgrade a library. A payment flow breaks. You change an API response. A mobile app crashes for users who never updated. You refactor a service. A downstream team loses a week. Backward compatibility is not a “nice engineering habit”. It is a must have. If your system cannot tolerate: – Old inputs – Old clients – Old contracts – Old behaviors Then you are not running a product. You are running a prototype in public. True engineering maturity looks like this: – You deprecate before you delete. – You version your APIs even when it feels slow. – You ship migrations with every breaking change. – You absorb the pain instead of passing it to users. – You support ugly legacy paths while building clean new ones. If you only know how to move fast by breaking things, you are not moving fast. You are borrowing speed on credit. And production always collects with interest.

Most dbt docs are either useless or unreadable. I've written and reviewed a lot of dbt documentation. It usually breaks one of two ways: too thin to help anyone, or too long for anyone to actually read. Here are 10 conventions I use to stay somewhere in between. 1. Write model descriptions so a non-technical stakeholder can understand them. 2. Use technical and business jargon carefully. Do not assume every reader knows the same terms. 3. Always document the staging layer (the inputs) and the mart layer (the outputs). 4. In intermediate models, you can skip passthrough columns. Focus on columns that introduce new logic. 5. Create consistent conventions for documenting common data types (IDs, dates, numbers, etc). 6. Describe timestamps and dates as: “When <something happened>.” 7. For booleans, explain both states: “TRUE when <...>” and “FALSE when <...>” 8. Prefer a complete description over a vague one-liner. 9. But remember: no one wants to read an essay for every column. 10. Re-read your documentation the next day. Rewrite anything that is not immediately clear. Good documentation does not explain the SQL. It explains what the data means.

I have just released the first Pipe Syntax best practices and style guide! 🐣 As GoogleSQL's Pipe Syntax gains traction, it is important to establish clear conventions for maintainable and efficient code. This guide aims to do just that with: ∙ 𝐂𝐨𝐫𝐞 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: data flow programming, micro-transformations, separation of concerns, and immutable data philosophy. ∙ 𝐂𝐨𝐝𝐞 𝐬𝐭𝐲𝐥𝐞 & 𝐫𝐞𝐚𝐝𝐚𝐛𝐢𝐥𝐢𝐭𝐲: vertical alignment, visual pipeline chunking, argument indentation, and consistent spacing. ∙ 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: strategic AS usage, AGGREGATE ordering, sequential TVF calls, and efficient filter/join placement. ∙ 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: hybrid queries, production output clarity, templated pipes, and view creation for reusability. ∙ 𝐎𝐭𝐡𝐞𝐫 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: adherence to standard SQL naming conventions, and preparing for future linter rules. I hope this guide will promote collaboration, reduce onboarding time, and enhance code reusability. 😊 👇 This link is in description 💡 It is still a living document, so I am actively soliciting and incorporating user feedback to ensure the guide remains relevant and useful. Feel free to give me your insights!

Want to grow in Data? Mastering File Formats is non-negotiable. Most beginners focus only on tools - SQL, Python, Spark. But real-world data work is 80% understanding how data is stored, shared, compressed, and moved. Here is a simple breakdown of the file formats every data professional must know - 1. Core Data Formats These are the foundations of almost every pipeline: CSV, JSON, Parquet, Avro - you’ll use them for storage, APIs, and analytics. 2. Big Data & Analytics Formats For large-scale workloads: ORC, Delta, HDF5, Feather/Arrow - built for speed, compression & massive datasets. 3. ML & Deep Learning Formats For training models and storing complex data: TFRecord, Pickle, Video/Audio formats, MAT files - essential for multimodal and scientific work. 4. Geospatial & Scientific Formats Used in climate, maps, and research: GeoJSON, NetCDF, Shapefiles - powerful for specialized domains. 5. System & Infra Formats For configs, backups, and microservices: YAML, Protocol Buffers, MessagePack, INI files, SQL dumps, HAR files - critical for engineering workflows. The deeper your understanding of file formats, the faster you grow as a Data Engineer, Analyst, or ML Engineer. Tools change, formats stay.

The magic of backward compatibility How your old USB stick still works with new laptop? How your old GPU still works in new motherboard? It’s because of the backward compatibility of these high speed interfaces. How do the old and new Devices “talk” to each other ? - When two devices connect, they don’t just start communicating at full speed. Instead, they negotiate capabilities and find the best common settings that both sides support. Let’s break it down using PCIe as example: - When a PCIe device (GPU, SSD, network card, etc.) is inserted into a PCIe slot, the link training process ensures that both the host and device find a common working configuration. PCIe Link Startup & Capability Exchange Steps 1️. Polling & Link Detection - The PCIe Root Complex (host) and Endpoint (device) detect each other by sending TS1 (Training Set 1) and TS2 (Training Set 2) Ordered Sets on all lanes. These Ordered Sets contain critical capability information, including: Supported PCIe Generations (Gen1 to Gen6, Gen7 is under development) Lane width (x1, x4, x8, x16) Device presence & clock settings 2️. Link Speed & Width Negotiation - Both sides compare their supported speeds and agree on the highest common speed. - Example: Old GPU (PCIe Gen2, x16) in New Motherboard (PCIe Gen4, x16) → The link trains at PCIe Gen2, x16. New SSD (PCIe Gen4, x4) in Old Motherboard (PCIe Gen3, x16) → The link trains at PCIe Gen3, x4. 3️. Equalization & Tuning (Gen3+ Only) - For PCIe Gen3 and later, the link goes through an equalization phase, where the transmitter (Tx) and receiver (Rx) adjust: Tx Presets & De-emphasis (to compensate for signal loss) Rx Adaptive Equalization (to filter out noise) - If signal integrity is bad at a higher speed (e.g., Gen4 and above), the link falls back to a lower stable speed. 4️. Finalizing Link & Entering Normal Operation (L0 State) - Once negotiation is complete, the link enters the L0 state, where actual data transfer begins. Why PCIe Backward Compatibility is So Powerful ? - No Forced Upgrades → You don’t need to replace everything when upgrading. - Dynamic Speed Scaling → If Gen4 doesn’t work due to bad signal integrity, the link automatically falls back to Gen3 or Gen2. - Flexible Lane Configuration → Even if a device supports x16, it can still work in an x8 or x4 slot, just with reduced bandwidth. 💡 Imagine if PCIe didn’t have this mechanism—every time you upgraded a motherboard, you’d need to replace your GPU, SSD, and networking cards! I am still using USB 2.0 based external drive purchased in the year 2009.

For years, product, engineering, and brand did most of the heavy lifting. But increasingly, growth is being shaped by whether an organization can deliver connected, personalized experiences across a customer journey that moves between digital and physical touchpoints effortlessly. Adobe and Incisiv’s automotive research, based on 560 executives across nine global markets, found that only 3% of the automotive customer journey is currently highly personalized. That is a remarkably small number given how much customers now expect every interaction to recognize who they are, what they have done before, and what they may need next. The same research found that 57% of leaders cite silos as their biggest challenge, yet only 4% say they have fully integrated customer data. That tells you why personalization remains so difficult in practice: disconnected systems still make it hard to create a consistent customer view, let alone act on it in real time. And many in our industry see AI as the silver bullet, but it is more of an amplifier. If the underlying data is fragmented, dealer and brand touchpoints are not connected, or accountability for journey orchestration is unclear, AI will expose those weaknesses more quickly. The road ahead is about unifying data, modernizing the platform foundation, and embedding governance and accountability into how customer experiences are actually delivered. #Automotive #CustomerExperience #AI 📸: Adobe