Update _posts/2025-10-17-introducing-arrow-avro.md

Co-authored-by: Andrew Lamb <andrew@nerdnetworks.org>

Author: jecsand838

_posts/2025-10-17-introducing-arrow-avro.md

@@ -37,7 +37,7 @@ The cost of sticking with a row‑centric path isn't only about CPU cycles; it a
 
 Modern pipelines magnify these needs because Avro isn't just a file format, it also shows up on the wire. Kafka ecosystems commonly use [Confluent Schema Registry's wire format](https://docs.confluent.io/platform/current/schema-registry/fundamentals/serdes-develop/index.html) and many services adopt [Avro Single‑Object Encodings](https://avro.apache.org/docs/1.11.1/specification/). In both cases, decoding into Arrow batches instead of per‑row values is what lets downstream compute stay vectorized and efficient.
 
-Within the Rust Arrow ecosystem, the `arrow-avro` effort was created specifically to address this gap by providing a vectorized Avro reader/writer that speaks Arrow natively and supports the major Avro container/framing modes in common use (Object Container Files, Single‑Object Encoding, and Confluent's wire format). Centralizing this logic upstream avoids bespoke, row‑based shims scattered across projects and sets a clearer path for evolution and maintenance. ([Arrow‑rs issue: "Add Avro Support"](https://github.com/apache/arrow-rs/issues/4886).
+Within the Rust Arrow ecosystem, the `arrow-avro` effort was created specifically to address this gap by providing a vectorized Avro reader/writer that speaks Arrow natively and supports the major Avro container/framing modes in common use (Object Container Files, Single‑Object Encoding, and Confluent's wire format). Centralizing this logic upstream makes it easier to collaborate with other projects, avoids bespoke, row‑based shims scattered across projects, and sets a clearer path for evolution and maintenance. ([Arrow‑rs issue: "Add Avro Support"](https://github.com/apache/arrow-rs/issues/4886).
 
 Development of an Arrow‑first Avro implementation upstream promises a simpler surface area for users and a more maintainable code path for contributors while opening the door to better integration for both batch and streaming clients.