2026-02-26 02:00:02
I recently came across an interesting challenge involving JSON decoding in Swift. Like many developers, when faced with a large, complex JSON response, my first instinct was to reach for “quick fix” tools. I wanted to see how our popular online resources — like Quicktype, various JSON-to-Swift converters, and even modern AI models — would handle a messy, repetitive data structure.
\ To be honest, I was completely underwhelmed.
The issue arises when you encounter a legacy API or a poorly structured response that uses “flat” numbered properties instead of clean arrays. Take a look at this JSON sample:
\
{
"meals": [
{
"idMeal": "52771",
"strMeal": "Spicy Arrabiata Penne",
"strInstructions": "Bring a large pot of water to a boil...",
"strMealThumb": "https://www.themealdb.com/images/media/meals/ustsqw1468250014.jpg",
"strIngredient1": "penne rigate",
"strIngredient2": "olive oil",
"strIngredient3": "garlic",
"strIngredient4": "chopped tomatoes",
"strIngredient5": "red chilli flakes",
// ... this continues up to strIngredient20
"strMeasure1": "1 pound",
"strMeasure2": "1/4 cup",
"strMeasure3": "3 cloves",
// ... this continues up to strMeasure20
}
]
}
When I plugged this into standard conversion tools, the result was a maintenance nightmare. They generated a “wall of properties” that looked something like this:
\
struct Meal: Codable {
let idMeal: String
let strMeal: String
let strInstructions: String?
let strMealThumb: String?
// The repetitive property nightmare
let strIngredient1: String?
let strIngredient2: String?
let strIngredient3: String?
// ...
let strIngredient20: String?
let strMeasure1: String?
let strMeasure2: String?
let strMeasure3: String?
// ...
let strMeasure20: String?
}
\ Let’s be honest, the code generated by those online tools belongs in the “trash bin” for any serious project. Not only is it unscalable, but imagine the look on your senior developer’s face during a PR review when they see 40+ optional properties. It’s a maintenance nightmare and a blow to your professional reputation.
\ I decided to take control of the decoding process to make it clean, Swifty, and — most importantly — production-ready. Here is how I structured the solution and why it works.
In 99% of Swift tutorials, you see CodingKeys defined as an enum. Enums are great when you know every single key at compile time. But in our case, we have a "flat" JSON with keys like strIngredient1, strIngredient2… up to 20. Writing an enum with 40 cases is not just boring — it’s bad engineering. That is why we use a struct instead.
To conform to CodingKey, a type must handle both String and Int values. By using a struct, we can pass any string into the initializer at runtime.
\
struct CodingKeys: CodingKey {
let stringValue: String
var intValue: Int?
init?(stringValue: String) {
self.stringValue = stringValue
}
// This allows us to map any raw string from the JSON to our logic
init(rawValue: String) {
self.stringValue = rawValue
}
init?(intValue: Int) {
return nil
}
// We don't need integer keys here
}
\
You don’t have to stick with the API’s naming conventions inside your app. Notice how I used static var to create aliases. This keeps the rest of the decoding logic readable while keeping the "dirty" API keys isolated inside this struct.
\
static var name = CodingKeys(rawValue: "strMeal")
static var thumb = CodingKeys(rawValue: "strMealThumb")
static var instructions = CodingKeys(rawValue: "strInstructions")
This is the part that makes this approach superior to any AI-generated code. We created static functions that use string interpolation to generate keys on the fly.
\
static func strIngredient(_ index: Int) -> Self {
CodingKeys(rawValue: "strIngredient\(index)")
}
static func strMeasure(_ index: Int) -> Self {
CodingKeys(rawValue: "strMeasure\(index)")
}
\
Instead of hardcoding strIngredient1, strIngredient2, etc., we now have a "key factory." When we loop through 1...20 in our initializer, we simply call these functions. It’s clean, it’s reusable, and it’s significantly harder to make a typo than writing 40 individual cases.
The original JSON treats an ingredient and its measurement as two strangers living in different houses. In our app, there are a couple. By nesting a dedicated struct, we fix the data architecture at the source:
\
struct Ingredient: Decodable, Hashable {
let id: Int
let name: String
let measure: String
}
\
Hashable and the id?I added an id property using the loop index. Why? Because modern SwiftUI views like List and ForEach require identifiable data. By conforming to Hashable, we ensure:
Before we get to the initializer, look at how we define our main properties. We aren’t just copying what the API gives us; we are translating it into Clean Swift.
\
let name: String
let thumb: URL?
let instructions: String
let ingredients: [Ingredient]
\
str Prefix: We dropped the Hungarian notation. name is better than strMeal.URL?. If the API sends a broken link or an empty string, our decoder handles it gracefully during the parsing phase, not later in the View.This is the finale. Instead of blindly accepting every key the JSON offers, our custom init(from:) acts like a bouncer at a club—only valid data gets in.
\
init(from decoder: any Decoder) throws {
let container = try decoder.container(keyedBy: CodingKeys.self)
// 1. Decode simple properties using our clean aliases
self.name = try container.decode(String.self, forKey: .name)
self.thumb = try? container.decode(URL.self, forKey: .thumb)
self.instructions = try container.decode(String.self, forKey: .instructions)
// 2. The Dynamic Decoding Loop
var ingredients: [Ingredient] = []
for index in 1...20 {
// We use 'try?' because some keys might be null or missing
if let name = try? container.decode(String.self, forKey: .strIngredient(index)),
let measure = try? container.decode(String.self, forKey: .strMeasure(index)),
!name.isEmpty, !measure.isEmpty {
// We only save it if the name AND measure are valid and non-empty
ingredients.append(Ingredient(id: index, name: name, measure: measure))
}
}
self.ingredients = ingredients
}
After all that work behind the scenes, look at what we’ve achieved. We have transformed a “flat” JSON nightmare into a model that is a joy to use. This is what the rest of your app sees now:
\
struct MealDetail {
let name: String
let instructions: String
let thumb: URL?
let ingredients: [Ingredient]
}
Because we did the heavy lifting during the decoding phase — filtering empty values and grouping ingredients — our SwiftUI code becomes incredibly clean. We don’t need any complex logic in the View; we just map the data directly to the components.
You might have noticed one small side effect: when we define a custom init(from: Decoder), Swift stops generating the default memberwise initializer. This can make writing unit tests or SwiftUI Previews a bit annoying.
\ To fix this and keep our codebase “test-friendly,” we can add this simple extension. This allows us to create “Mock” data for our UI without needing a JSON file.
\
extension MealDetail {
// Restoring the ability to create manual instances for Mocks and Tests
init(name: String, thumb: URL?, instructions: String, ingredients: [Ingredient]) {
self.name = name
self.thumb = thumb
self.instructions = instructions
self.ingredients = ingredients
}
}
\
Now, creating a preview is as simple as: let mock = MealDetail(name: "Pasta", thumb: nil, instructions: "Cook it.", ingredients: [])
The next time you’re faced with a messy API, remember: don’t let the backend dictate your frontend architecture. Online tools and AI might give you a quick “copy-paste” solution, but they often lead to technical debt. By taking control of your Decodable implementation, you create code that is:
\ Happy coding, and keep your models clean!
\ Full code is here:
2026-02-26 01:34:27
Alisa Viejo, CA, United States, February 25th, 2026/CyberNewswire/---One Identity, a trusted leader in identity security, today announced the appointment of Michael Henricks as Chief Financial and Operating Officer. This decision reflects the continued growth of the business and a focus on aligning financial leadership with operational objectives as One Identity scales.
“As One Identity accelerates its growth, the addition of a Chief Financial and Operating Officer will strengthen how we plan, operate, and invest across the business,” said Praerit Garg, CEO of One Identity. “As identity security becomes fundamental to how organizations operate, our focus is on making it simpler, more resilient, and easier to deploy at scale. Michael brings deep experience guiding companies through periods of rapid growth, operational change, and complexity. His leadership will be critical as we continue to serve and delight our customers worldwide.”
Henricks brings more than 30 years of experience across technology, business services, and financial services organizations. Prior to joining One Identity, Henricks held senior financial leadership roles at private equity-backed and technology-driven organizations. Most recently, he served as Chief Executive Officer and previously Chief Financial Officer at Momentive Software. He has worked closely with executive teams to strengthen operating discipline and improve decision-making.
As Chief Financial and Operating Officer, Henricks’ role will extend beyond the traditional CFO responsibilities of financial stewardship and into strategic planning and operational-financial integration. He will oversee global finance operations, leading financial planning, operational efficiency, and long-term strategy as One Identity continues to expand its footprint.
“One Identity sits at the heart of how modern organizations operate securely, and that’s what makes this role so compelling,” said Henricks. “With identity as the foundation for digital trust, customers need platforms that are not only secure, but reliable, scalable, and built for real-world complexity. I’m excited to join a team that has earned deep trust in the market and to help ensure the business continues to deliver for customers as they grow, modernize, and adapt.”
Henricks’ appointment comes as One Identity continues to expand its global customer base, supporting more than 11,000 organizations worldwide and managing over 500 million identities. With customer satisfaction consistently measured at 97 percent, the company is investing strategically in leadership, product development, and go-to-market execution as it scales.
One Identity is strengthening its ability to deliver secure, reliable identity solutions at enterprise scale – whether organizations are adopting SaaS-first approaches, incorporating AI and automation, or running hybrid or self-managed environments.
One Identity delivers trusted identity security for enterprises worldwide to protect and simplify access to digital identities. With flexible deployment options and subscription terms – from self-managed to fully managed – our solutions integrate seamlessly into your identity fabric to strengthen your identity perimeter, protect against breaches, and ensure governance and compliance.
Trusted by more than 11,000 organizations managing over 500 million identities, One Identity is a leader in identity governance and administration (IGA), privileged access management (PAM), and access management (AM) for security without compromise.
Users can learn more at www.oneidentity.com.
Liberty Pike
One Identity LLC
:::tip This story was published as a press release by Cybernewswire under HackerNoon’s Business Blogging Program
:::
Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
\
2026-02-26 01:14:56
Harlow, Essex, United Kingdom — 25 February 2026 — Comdex Data Services Limited has announced the launch of Comdex TraceOS™, its proprietary blockchain intelligence platform designed to trace cryptocurrency fund flows, assess risk signals, support fraud prevention workflows, and accelerate recovery work for retail scam victims in the United States.
The launch comes as US agencies continue to warn about the scale and growth of crypto-enabled fraud. The FBI describes cryptocurrency investment fraud, commonly referred to as “pig butchering,” as one of the most prevalent and damaging fraud schemes.
FinCEN has also issued an alert highlighting “pig butchering” as a prominent virtual currency investment scam and outlining indicators for identifying related activity. The US Secret Service has published a public advisory describing “pig butchering” scam methods and prevention guidance. Separately, the FTC reported that investment scams generated the highest reported losses of any fraud category in 2024.
According to Comdex, Comdex TraceOS™ was developed entirely in-house and uses AI and machine learning for pattern recognition across blockchain activity.
The system is designed to support investigations across major cryptocurrencies and networks, including widely used assets such as Bitcoin (BTC), Ethereum (ETH) and Solana (SOL), as well as token ecosystems frequently associated with newer scam typologies. The platform is operated by Comdex investigators and is not offered as a consumer-facing product.
Comdex said the platform consolidates several investigation and recovery workflows in one system. These include wallet clustering and attribution, fund-flow tracing across wallets and services, automated risk scoring, cross-chain tracing through swaps and bridges, scam pattern detection, and case-ready forensic reporting intended to support engagement with exchanges and law enforcement.
Comdex said its services are focused on assisting retail victims of crypto loss linked to common fraud patterns. These include romance and “pig butchering” scams, fake trading platforms and fraudulent exchanges, impersonation of customer support, airdrop and giveaway scams, phishing and wallet drains, SIM swap attacks, rug pulls and exit scams that target people after an initial loss. The FBI’s IC3 has also warned that fraudsters often initiate contact through social media or dating applications and use fictitious returns to encourage additional deposits.
Comdex said the platform supports asset tracing and recovery activity, including cross-border coordination where required, and is designed to speed up identification of on-chain routes to service providers and exchanges. The company said it aims to reduce time-to-action by using AI-enabled scanning to trace funds to exchange and service touchpoints more quickly.
Comdex said it has handled thousands of cases over approximately 11 years of crypto-related recovery work, including partial and full recoveries. The company said it has recovered more than $150 million using third-party software and external tooling, and that it began building its own technology in 2023 based on its casework experience to improve tracing speed, risk detection, and recovery execution. Comdex reports an 89.4% success rate on a no-win, no-fee basis, supported by internal reporting and independent audit activity.
Looking ahead, Comdex said it estimates that it could support recoveries totaling upwards of $450 million by 2035, driven by AI-enabled scanning across blockchains and faster tracing to exchanges and other on-ramps and off-ramps. Comdex also said it plans to explore post-quantum approaches over the next two decades as part of its long-term research and development roadmap, with the goal of strengthening resilience against future cyber-enabled fraud.
Comdex said its operating model is designed to take complexity out of recovery for victims, using structured intake, evidence handling, and clear case updates during time-sensitive tracing and preservation windows.
Media enquiries
Comdex Data Services LimitedWebsite: comdexdataservices.comEmail: [email protected]
Comdex Data Services Limited is a UK private limited company incorporated on 10 December 1997 (company number 03478499) with a registered office at 18 New Horizon Business Centre, Barrows Road, Harlow, Essex, CM19 5FN. Comdex provides crypto tracing and recovery support for retail scam victims using its proprietary blockchain intelligence capabilities, including Comdex TraceOS™.
:::tip This story was published as a press release by Btcwire under HackerNoon’s Business Blogging Program
\ :::
Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
\
2026-02-26 00:48:31
Apia, Samoa, February 25, 2026 — Phemex, a user-first crypto exchange, unveiled the AI Bot, a milestone of the Phemex AI-Native Revolution, following its transition into an AI-native organization. This launch evolves artificial intelligence from a strategic vision into a high-performance "Intelligent Trading Partner," shifting the industry paradigm from emotional manual execution to a disciplined "Human + AI Collaboration" model for its 10 million users worldwide.
Earlier this year, Phemex introduced its AI-Native Initiative, committing to integrate artificial intelligence across internal operations and external product architecture. The launch of AI Bot serves as a live demonstration of that strategy in practice, moving beyond conceptual transformation into user-facing applications.
Utilizing advanced machine learning to analyze millions of data points in real-time, the Phemex AI Bot automates complex quantitative strategies across Futures Grid, Spot Grid, and Martingale systems. Engineered with a "Risk-Aware Intelligence" , the engine prioritizes capital preservation by dynamically adjusting leverage and parameters based on historical volatility. This ensures that intelligence remains a tool for resilience, allowing traders to gain significant leverage from AI rather than losing their competitive edge to it.
To catalyze this era of intelligent trading, Phemex has initiated the AI Bot Carnival, a $1,000,000+ trading feast. The initiative features a 100% Loss Protection Program for newcomers to ensure a zero-barrier entry into quantitative trading, alongside tiered volume rewards up to 5,000 USDT and multi-bot incentives designed to encourage systematic, diversified portfolio management.
"Phemex AI Bot is solid proof that our AI-Native strategy is not theoretical — it is operational," said Federico Variola, CEO of Phemex. "We are not experimenting with AI at the margins. We are actively building an exchange where intelligent systems are embedded into how products function. This launch is an early but concrete step, and we will continue executing this long-term strategy."
With AI Bot now live, Phemex advances its roadmap toward a fully AI-native exchange model — where intelligence is integrated at the infrastructure level and progressively embedded into the trading experience.
About Phemex
Founded in 2019, Phemex is a user-first crypto exchange trusted by over 10 million traders worldwide. The platform offers spot and derivatives trading, copy trading, and wealth management products designed to prioritize user experience, transparency, and innovation. With a forward-thinking approach and a commitment to user empowerment, Phemex delivers reliable tools, inclusive access, and evolving opportunities for traders at every level to grow and succeed.
For more information, please visit: https://phemex.com/
Media contact:Oyku YavuzPR [email protected]
:::tip This story was published as a press release by Blockmanwire under HackerNoon’s Business Blogging Program
\ :::
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
\
2026-02-26 00:45:02
dbt, Airflow, Spark, and plain SQL walked into a bar. Only one of them didn't cause an incident at 2 am.
It started with a straightforward ask.
Our analytics team needed a daily pipeline: pull raw event data from S3, clean it, join it against a customer dimension table, aggregate it into a revenue summary, and land it in the warehouse by 7 am so the business had numbers for their morning standup.
Simple enough. Except I had four strong opinions in my head and a rare window of time where I could actually test them properly.
So instead of picking one approach and moving on, I built the same pipeline four times. Same source data. Same business logic. Same destination. Different tools, different architectures, different everything else.
Three months later, I have opinions I didn't have before. Some of them surprised me.
Every data team I've worked with has the same unspoken war going on: the engineers want Spark, the analytics engineers want dbt, the data scientists want Airflow DAGs they can control themselves, and somewhere a senior engineer has a 3,000-line SQL file they wrote in 2019 that nobody is allowed to touch because it somehow still works.
These debates are usually settled by whoever speaks loudest in the architecture meeting, not by evidence. I wanted evidence.
Here's what the logic actually needed to do:
Read raw clickstream events from S3 (JSON, roughly 40 million rows per day, schema drifts occasionally). Deduplicate events, since the same event_id can appear multiple times due to at-least-once delivery. Filter to only revenue-generating event types. Join against a slowly-changing customer dimension table (Type 2 SCD). Aggregate to daily revenue per customer segment. Write the result to a warehouse table with incremental logic so we're not reprocessing everything every day.
Nothing exotic. Exactly the kind of pipeline that exists in thousands of companies right now.
I'll be honest. I almost didn't include this one. It felt too old-fashioned to take seriously.
I was wrong to feel that way.
The SQL version took the least time to write. The business logic was completely transparent. Anyone who could read SQL could follow it. The deduplication was a ROWNUMBER() window function. The SCD join was a date-bound BETWEEN. The incremental logic was a WHERE eventdate = CURRENT_DATE - 1.
It ran in 4 minutes in our warehouse. Costs almost nothing. When it breaks, the error message tells you exactly which line failed.
What went wrong: schema drift broke it on day 11. A new event type arrived with a field that had a different data type than expected, and the pipeline failed silently. It completed successfully. It just dropped those rows. I didn't know for two days. There was no alerting, no data quality check, no contract enforcement. Just missing revenue numbers that everyone assumed were a business dip.
That was the lesson. SQL is not the problem. The lack of structure around SQL is the problem. When something goes wrong, you're debugging alone with SELECT * and a prayer.
Would I use it again? For stable, well-understood pipelines with strong testing upstream, yes. For anything touching raw external data, absolutely not without wrapping it in something with validation and observability.
Airflow felt like the responsible adult choice. Scheduling, retries, dependencies, a UI you can actually look at. What's not to like?
Building the DAG took longer than I expected. Not because Airflow is hard, but because I kept running into the gap between the Python that orchestrates the work and the Python that does the work. Airflow is an orchestrator, not an execution engine. That's the right design, but it meant my actual transformation logic lived in a jumble of SQL strings embedded in Python operators, and that combination is harder to test than either pure SQL or pure Python would be.
The SCD in particular got ugly. Expressing slowly-changing dimension logic in a way that's both correct and readable inside a PythonOperator is an exercise in patience.
What actually worked: the observability was genuinely excellent. When the S3 read failed on day 6 because of a permissions issue, I had an email in my inbox before I'd finished my coffee. The retry logic handled two transient failures automatically. The audit trail told me exactly when each task ran, how long it took, and what the inputs were.
What went wrong: dependency management became a burden fast. The Airflow environment needed specific package versions, and two of those packages conflicted with each other in ways that took an entire afternoon to diagnose. And the DAG code became hard to read quickly. By the end, it was 380 lines of Python to express logic that took 90 lines of SQL.
Also, testing Airflow DAGs locally is a genuinely miserable experience. I spent more time fighting the local environment than writing business logic.
Would I use it again? Yes, but as pure orchestration, calling out to dbt or a separate transformation layer. Never again as the place where transformation logic lives.
Spark is the one who impresses people in interviews. It's also the one that humbled me the most.
The PySpark code was actually elegant. Dataframe operations for deduplication, a broadcast join for the customer dimension since that table fits in memory, wand indowing functions for the SCD logic. When it worked, it was fast and expressive.
For 40 million rows a day, Spark is overkill. I knew this going in. But I wanted to understand the real cost of that overkill, not just assume it.
The real cost: cluster startup time added 4 to 6 minutes to every run before a single row was processed. My SQL version ran in 4 minutes total. The Spark version ran in 3 minutes of actual processing plus 5 minutes of cluster initialization. So it was "faster" in the wrong way.
The operational overhead was the bigger problem. Spark has opinions about memory management that it enforces aggressively and explains poorly. I hit an out-of-memory error on day 8 during a backfill run, and diagnosing it required reading Spark executor logs in a UI that feels like it was designed to discourage you.
What actually worked: the schema enforcement was excellent. I defined a schema explicitly, and when the drift event hit on day 11, the same one that silently broke the SQL version, Spark threw an error immediately and loudly. Nothing got silently dropped.
Would I use it again? For this use case, no. For data at 10x or 100x this volume, or for complex transformations that a SQL engine can't express cleanly, yes. Spark earns its complexity at scale. At 40 million rows, it's a sports car you're using to drive to the grocery store.
I'll admit my bias upfront. I came into this expecting dbt to win. I was not entirely right, and the ways I was wrong were instructive.
The dbt version felt the most like software engineering. Models are files. Files are version-controlled. Tests are declarative. The documentation is generated automatically. When I wrote the revenue aggregation model, I could see exactly which upstream models it depended on, and dbt would refuse to run it if those models hadn't succeeded first.
The SCD join was clean. dbt's snapshot feature handles Type 2 SCDs with a configuration block, no manual date-bounding required. The incremental model logic was readable and explicit. The deduplication was a simple macro.
What actually worked: the governance story was the best of any approach. Column-level descriptions, source freshness alerts, test coverage on every model, and a lineage graph I could show to a non-technical stakeholder. When the schema drift hit, a dbt test caught it before the model ran.
What went wrong: dbt is opinionated and sometimes its opinions fought mine. Incremental logic that seems simple in concept has edge cases that the is_incremental() macro handles in ways that aren't always obvious. I hit a subtle issue where late-arriving events from the previous day weren't being reprocessed correctly and debugging it required understanding dbt internals I hadn't needed to know before.
The other thing: dbt runs SQL. It does not run Python. For anything that genuinely needs Python, complex ML feature engineering, calling external APIs mid-pipeline, you're reaching for dbt-python models or pushing that logic elsewhere. That boundary trips people up.
Would I use it again? Yes, and it's now my default for analytical pipelines in a warehouse environment. But I'd pair it with an orchestrator for scheduling and alerting, and I'd be honest with my team that dbt's learning curve is real and the documentation, while good, assumes you already think a certain way about data modeling.
| | Build Time | Debuggability | Schema Safety | Operational Overhead | Governance | |----|----|----|----|----|----| | Plain SQL | Fast | Low | None | Very Low | None | | Airflow | Slow | High | Low | High | Low | | Spark | Medium | Low | High | Very High | Low | | dbt | Medium | Medium | High | Medium | High |
Never embed transformation logic inside an Airflow DAG. Airflow is an orchestrator. Treat it like one. The moment your SQL lives inside a Python string inside an operator, you've created something harder to test than SQL and harder to debug than Python. Keep orchestration and transformation separate.
Never use Spark below a certain data volume threshold without justifying it in writing. Not because Spark is bad, but because the operational tax is real and it compounds. Every new team member needs to learn it. Every environment needs to support it. Every incident takes longer to diagnose. That's fine if you're processing petabytes. It's a waste if you're not.
Never deploy plain SQL against raw external data without schema contracts. SQL is fast to write and easy to read. It is not self-defending. One upstream schema change and you're flying blind. If you're going to use SQL, pair it with something that enforces contracts and makes failures loud.
Never pick a tool based on what's impressive. Spark impresses people. It impressed me in the demo phase. But the right tool is the one your team can operate, debug, and extend at 2 am when something breaks, and the business is waiting on numbers. Boring infrastructure is underrated.
The pipeline wasn't the hard part. It never is.
The hard part is what happens after the pipeline runs. Is the data trustworthy? Would you know if it wasn't? Can someone else on your team understand what it's doing? Can you audit it when the business asks why a number changed?
None of the four approaches nailed all of that on their own. The one that came closest was dbt, not because it's magic, but because it's designed around the assumption that data pipelines exist in organizations and organizations need documentation, testing, and lineage more than they need raw speed.
Three months of building the same thing four ways taught me less about tools than I expected and more about what questions to ask before picking one.
Start with: what does "this broke" look like at 2 am, and who's going to fix it?
The answer tells you everything about which tool you should choose.
\
2026-02-26 00:34:18
PANAMA CITY, February 25, 2026 – BingX, a leading cryptocurrency exchange and Web3-AI company, announced the full integration of BingX TradFi into the broader BingX ecosystem, marking a significant step in the convergence of traditional finance and crypto markets.
This development reflects a broader industry trend projected for 2026: traditional finance is increasingly embracing cryptocurrencies, while the crypto sector continues to integrate with traditional finance.
BingX is positioned at the center of this structural shift:
“The full integration of BingX TradFi into our ecosystem represents a structural evolution in global markets." said Vivien Lin, Chief Product Officer at BingX. "We are witnessing a two-way convergence: traditional finance is embracing digital assets, while crypto infrastructure is maturing to support real-world financial instruments at scale. By embedding TradFi across perpetual futures, copy trading, AI tools, spot markets, and VIP services, BingX is building a unified platform where users can navigate multiple markets efficiently, intelligently, and without friction.”
\
Founded in 2018, BingX is a leading crypto exchange and Web3-AI company, serving over 40 million users worldwide. Ranked among the top five global crypto derivatives exchanges and a pioneer of crypto copy trading, BingX addresses the evolving needs of users across all experience levels.
Powered by a comprehensive suite of AI-driven products and services, including futures, spot, copy trading, and TradFi offerings, BingX empowers users with innovative tools designed to enhance performance, confidence, and efficiency.
BingX has been the principal partner of Chelsea FC since 2024, and became the first official crypto exchange partner of Scuderia Ferrari HP in 2026.
For media inquiries, please contact: [email protected]
For more information, please visit: https://bingx.com/
:::tip This story was published as a press release by Blockmanwire under HackerNoon’s Business Blogging Program
\ :::
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
\
