Update testbricks.netlify.app copy for SparkProxy and Lakeflow

docs/website-landing-copy.md

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+# Landing page copy for [testbricks.netlify.app](https://testbricks.netlify.app/)
+
+Drop-in files for `engineeringmadness/testbricks-website` (this library repo cannot push that repository):
+
+- `docs/website/index.html` → website `index.html`
+- `docs/website/App.tsx` → website `src/App.tsx`
+
+The live site still uses the old `SparkMock` name in examples. Marketing copy stays high-level; the code tabs show the current API.
+
+## Meta (`index.html`)
+
+**title:** `testbricks — Run Databricks workflows locally`
+
+**description:**
+
+> testbricks runs Databricks workflows on your laptop with a Spark proxy, a drop-in dbutils, and a workflow runner.
+
+**og:description:**
+
+> Run Databricks workflows on your laptop with SparkProxy, a dbutils mock, and a JSON workflow runner.
+
+## Hero
+
+**h1:** Run Databricks Workflows E2E / **in your Local Environment** (unchanged)
+
+**body:**
+
+> testbricks is a Python library with genuinely useful mocks — a Spark proxy that reads and writes tables as CSV, a drop-in `dbutils` replacement, and a runner that executes a whole workflow JSON in dependency order. No cluster. No waiting around.
+
+**install:** `pip install testbricks`
+
+**fine print:** `Python 3.10+ · works in notebooks, scripts and CI`
+
+## Features
+
+**SparkProxy**
+
+> A SparkSession stand-in that speaks the same API. Table reads and writes land as CSV files on disk — no cluster, no metastore, no waiting.
+
+**Drop-in dbutils**
+
+> Widgets, filesystem helpers, secrets, and more. Import it instead of the real thing and your notebook runs unchanged.
+
+**Workflow runner**
+
+> Parses a Databricks workflow JSON, builds the task graph, and runs notebooks in the right order on your machine.
+
+**Zero cluster time**
+
+> Iterate in seconds on your laptop. Debug with breakpoints, run it in CI, and keep your compute bill for the things that matter.
+
+## How it works
+
+1. **Swap in the mocks** — Replace the Spark session and dbutils object with the testbricks equivalents at the top of your notebook.
+2. **Drop your data in a folder** — CSV files in a base directory stand in for your tables. Read, write and inspect them with any tool you like.
+3. **Run the whole workflow** — Hand the runner your workflow JSON and it walks the dependency graph, notebook by notebook, right on your machine.
+
+## Code examples (`SNIPPETS` in `App.tsx`)
+
+First tab label: `SparkProxy` (not `SparkMock`).
+
+### SparkProxy
+
+**blurb:** Point it at a folder. Table reads and writes land as plain CSV files you can open anywhere.
+
+```python
+from testbricks import SparkProxy
+
+spark = SparkProxy("./data")
+
+df = spark.read.option("header", "true").option("inferSchema", "true").table("bronze.customers")
+df.write.mode("overwrite").saveAsTable("silver.customers_enriched")
+```
+
+### dbutils
+
+**blurb:** A drop-in replacement for the Databricks dbutils object. Notebooks the runner executes get it automatically.
+
+```python
+from testbricks.dbutils import configure, dbutils
+
+configure("./data")  # same catalog root as SparkProxy
+
+dbutils.widgets.text("filter_country", "USA")
+country = dbutils.widgets.get("filter_country")
+
+for info in dbutils.fs.ls("/"):
+    print(info.name, info.size)
+```
+
+### LocalWorkflowRunner
+
+**blurb:** Feed it your exported workflow JSON. It resolves the task graph and runs every notebook in dependency order.
+
+```python
+from testbricks import SparkProxy, LocalWorkflowRunner
+
+spark = SparkProxy("./data")
+runner = LocalWorkflowRunner(
+    source_dir="./notebooks",
+    workflow_json_path="./workflow.json",
+    base_path="./data",
+)
+runner.run_workflow(extra_globals={"spark": spark})
+```