GITBOOK-428: show Python and JSON as alternatfiives, not steps

Author: Chandanydv1234

docs/community-and-enterprise-editions/community-vs-enterprise/README.md

@@ -8,7 +8,7 @@ description: >-
 
 Every Zingg edition is built on the same foundation: probabilistic matching, active learning, and warehouse-native execution. What changes is the identity infrastructure, operational control, and advanced matching capability you obtain on top of it.
 
-<table><thead><tr><th width="148.5625" valign="top"></th><th valign="top">Community</th><th valign="top">Enterprise Lite</th><th valign="top">Enterprise</th></tr></thead><tbody><tr><td valign="top">Who it is for</td><td valign="top">Teams that are evaluating Zingg, running proofs of concept, or do not need a persistent identity graph with continously updated data.</td><td valign="top">Teams that want to run entity resolution into production and need persistent entity IDs, deterministic matching and incremental updates.</td><td valign="top">Teams who need full lifecycle control: seamless model upgrades, governance, explainability, and dictionary-based matching for complex data.</td></tr><tr><td valign="top">Identity graph</td><td valign="top"><code>Z Cluster</code> is a unique cluster identifier assigned per run.</td><td valign="top">Persistent <code>Zingg ID</code> (GUID). Stable across runs and safe to reference in CRMs, warehouses, and pipelines.</td><td valign="top">Persistent <code>Zingg ID</code> plus seamless ID reassignment when you upgrade your model. Downstream systems stay intact across changes.</td></tr><tr><td valign="top">Matching capability</td><td valign="top">Probabilistic matching trained on your labeled pairs. Handles typos, abbreviations, and variations across any field type.</td><td valign="top">Probabilistic + deterministic matching in a single flow. Hard rules for trusted identifiers like SSN, tax ID, email. <code>Pass Through</code> for records that should appear in output but not influence matching.</td><td valign="top"><p>Everything in Lite, plus dictionary-based matching (nicknames, aliases, company name variants).</p><p>Match statistics and explainability for audit and governance.</p></td></tr><tr><td valign="top">Model operations</td><td valign="top">Train once and match as data arrives.</td><td valign="top"><ul><li>Incremental runs update the identity graph as new records arrive without retraining.</li><li>Verify blocking before committing to matching.</li></ul></td><td valign="top"><p>Everything in Lite, plus<br></p><ul><li>Upgrade models without disrupting <code>Zingg ID</code>s.</li><li>Compare two trained models side by side before deploying.</li><li>Re-assign IDs from an old model to a new one.</li></ul></td></tr><tr><td valign="top">Platform</td><td valign="top">Any Spark environment - Databricks, Fabric, EMR, GCP Dataproc, and local Spark.</td><td valign="top">All Community platforms plus<br>Native Snowflake run (no Spark cluster required)<br><br></td><td valign="top">All platforms in Lite.</td></tr></tbody></table>
+<table><thead><tr><th width="148.5625" valign="top"></th><th valign="top">Community</th><th valign="top">Enterprise Lite</th><th valign="top">Enterprise</th></tr></thead><tbody><tr><td valign="top">Who it is for</td><td valign="top">Teams that are evaluating Zingg, running proofs of concept, or do not need a persistent identity graph with continuously updated data.</td><td valign="top">Teams that want to run entity resolution into production and need persistent entity IDs, deterministic matching and incremental updates.</td><td valign="top">Teams who need full lifecycle control: seamless model upgrades, governance, explainability, and dictionary-based matching for complex data.</td></tr><tr><td valign="top">Identity graph</td><td valign="top"><code>Z Cluster</code> is a unique cluster identifier assigned per run.</td><td valign="top">Persistent <code>Zingg ID</code> (GUID). Stable across runs and safe to reference in CRMs, warehouses, and pipelines.</td><td valign="top">Persistent <code>Zingg ID</code> plus seamless ID reassignment when you upgrade your model. Downstream systems stay intact across changes.</td></tr><tr><td valign="top">Matching capability</td><td valign="top">Probabilistic matching trained on your labeled pairs. Handles typos, abbreviations, and variations across any field type.</td><td valign="top">Probabilistic + deterministic matching in a single flow. Hard rules for trusted identifiers like SSN, tax ID, email. <code>Pass Through</code> for records that should appear in output but not influence matching.</td><td valign="top"><p>Everything in Lite, plus dictionary-based matching (nicknames, aliases, company name variants).</p><p>Match statistics and explainability for audit and governance.</p></td></tr><tr><td valign="top">Model operations</td><td valign="top">Train once and match as data arrives.</td><td valign="top"><ul><li>Incremental runs update the identity graph as new records arrive without retraining.</li><li>Verify blocking before committing to matching.</li></ul></td><td valign="top"><p>Everything in Lite, plus<br></p><ul><li>Upgrade models without disrupting <code>Zingg ID</code>s.</li><li>Compare two trained models side by side before deploying.</li><li>Re-assign IDs from an old model to a new one.</li></ul></td></tr><tr><td valign="top">Platform</td><td valign="top">Any Spark environment - Databricks, Fabric, EMR, GCP Dataproc, and local Spark.</td><td valign="top">All Community platforms plus<br>Native Snowflake run (no Spark cluster required)<br><br></td><td valign="top">All platforms in Lite.</td></tr></tbody></table>
 
 ### When to choose each edition
 

docs/connect-your-data/connect-cloud-storage.md

@@ -157,7 +157,7 @@ Setting `zinggDir` to an S3 path stores all Zingg model files and training data
 {% endtab %}
 
 {% tab title="Azure Blob" %}
-_**CHECK WITH SONAL - - NEED TEAMS HELP TO CHECK WHAT EXACTLY TO BE ADDED HERE**_
+
 {% endtab %}
 
 {% tab title="GCS" %}

docs/connect-your-data/connect-file-formats.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Configure Zingg pipes for CSV, Parquet,  Avro, JSON, TSV, and XML flat file
+  Configure Zingg pipes for CSV, Parquet, Avro, JSON, TSV, and XML flat file
   formats.
 ---
 
@@ -185,10 +185,6 @@ args.setData(jsonPipe)
 * For JSON Lines (one object per line), no additional props needed.
 {% endhint %}
 {% endtab %}
-
-{% tab title="XML" %}
-_**XML connector documentation is being confirmed. This section will be updated with the JSON config and Python API once verified**_
-{% endtab %}
 {% endtabs %}
 
 <details>

docs/connect-your-data/connect-graph-databases-neo4j.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Configure Zingg to connect to Neo4j  for writing resolved entity output  to a
+  Configure Zingg to connect to Neo4j for writing resolved entity output to a
   graph database.
 ---
 

docs/connect-your-data/connect-nosql-databases.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Connect Zingg to Cassandra and MongoDB  as data sources and output
+  Connect Zingg to Cassandra and MongoDB as data sources and output
   destinations.
 ---
 

docs/connect-your-data/connect-relational-databases.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Connect Zingg to MySQL, PostgreSQL,  SQL Server, DB2, MariaDB, and Oracle  via
+  Connect Zingg to MySQL, PostgreSQL, SQL Server, DB2, MariaDB, and Oracle via
   JDBC.
 ---
 

docs/contributing/setting-up-zingg-development-environment/ubuntu-wsl2-setup-guide.md

@@ -203,4 +203,4 @@ Run this script in the terminal opened in Zingg clones directory `./scripts/zing
 
 Run this script in the terminal opened in Zingg clones directory `./scripts/zingg.sh --phase match --conf examples/febrl/config.json --properties-file config/zingg.conf`
 
-Change the directory `cd /tmp/zinggOutput` (path provided in the configconfig file) to see the output files.
+Change the directory `cd /tmp/zinggOutput` (path provided in the config file) to see the output files.

docs/entity-resolution/README.md

@@ -93,7 +93,7 @@ This process is the Identity RAG pattern entity resolution as infrastructure for
 
 <summary><strong>Fraud Detection</strong></summary>
 
-Fraudulent actors create multiple accounts or profiles with small variations in identity fields to evade detection different email formats, transposed date of birth, and slight name variations.
+Fraudulent actors create multiple accounts or profiles with small variations in identity fields to evade detection—different email formats, transposed date of birth, and slight name variations.
 
 Entity resolution identifies when records that look different actually belong to the same entity, surfacing hidden connections that threshold-based or rule-based systems miss.
 
@@ -105,7 +105,7 @@ Zingg's probabilistic matching is calibrated to detect these deliberate variatio
 
 <summary><strong>KYC and AML</strong></summary>
 
-Know Your Customer and Anti-Money Laundering workflows require matching incoming data against existing records, sanctions lists, and watchlists often across inconsistent name formats, transliterations, and missing fields.
+Know Your Customer and Anti-Money Laundering workflows require matching incoming data against existing records, sanctions lists, and watchlists, often across inconsistent name formats, transliterations, and missing fields.
 
 Entity resolution handles these variations systematically, reducing false positives and ensuring compliance teams review the right cases rather than being overwhelmed by noise.
 

docs/frequently-asked-questions/frequently-asked-questions.md

@@ -12,7 +12,7 @@ description: >-
 
 <summary><strong>What kind of preprocessing and data cleaning do I need before running Zingg?</strong></summary>
 
-Zingg does not mandate any preprocessing on the input data. Zingg automatically takes care of extra spaces, punctutation characters and cases so you dont have to.
+Zingg does not mandate any preprocessing on the input data. Zingg automatically takes care of extra spaces, punctuation characters and cases so you don't have to.
 
 </details>
 

docs/interpreting-results/explainability-and-statistics.md

@@ -25,7 +25,7 @@ in explain output at this time. Clusters formed primarily through deterministic
 If you get empty results, verify that the Zingg ID you are querying exists in your match output before running explain.
 {% endhint %}
 
-Explain answers "why did these specific records end up together?" for one cluster at a time. If you instead want aggregate, run-level visibility , how many clusters formed, how dense they are, how much of the matching came from deterministic rules versus probabilistic scoring, and whether clusters are stable across incremental runs , that's what **output statistics** provides, at three levels: a run-wide **Summary**, per-**Cluster** detail, and per-**Record** detail.
+Explain answers "why did these specific records end up together?" for one cluster at a time. If you instead want aggregate, run-level visibility, how many clusters formed, how dense they are, how much of the matching came from deterministic rules versus probabilistic scoring, and whether clusters are stable across incremental runs, that's what **output statistics** provides, at three levels: a run-wide **Summary**, per-**Cluster** detail, and per-**Record** detail.
 
 {% hint style="success" icon="right-long" %}
 **Read more**: