diff --git a/dandiapi/analytics/tasks/__init__.py b/dandiapi/analytics/tasks/__init__.py
index 1e1ba3d16..3314bc305 100644
--- a/dandiapi/analytics/tasks/__init__.py
+++ b/dandiapi/analytics/tasks/__init__.py
@@ -10,6 +10,7 @@
 from django.db.models.aggregates import Max
 from django.db.models.expressions import F
 from django.db.utils import IntegrityError
+from more_itertools import batched
 from s3logparse import s3logparse
 
 from dandiapi.analytics.models import ProcessedS3Log
@@ -80,10 +81,19 @@ def process_s3_log_file_task(s3_log_key: str) -> None:
                 logger.info('Already processed log file %s', s3_log_key)
             return
 
+        # we need to store all of the fully hydrated blob objects in memory in order to use
+        # bulk_update, but this turns out to not be very costly. 1,000 blobs use about ~8kb
+        # of memory.
+        asset_blobs = []
+
+        # batch the blob queries to avoid a large WHERE IN clause
+        for batch in batched(download_counts, 1_000):
+            asset_blobs += AssetBlob.objects.filter(blob__in=batch)
+
+        for asset_blob in asset_blobs:
+            asset_blob.download_count = F('download_count') + download_counts[asset_blob.blob]
+
         # note this task is run serially per log file. this is to avoid the contention between
         # multiple log files trying to update the same blobs. this serialization is enforced through
         # the task queue configuration.
-        for blob, download_count in download_counts.items():
-            AssetBlob.objects.filter(blob=blob).update(
-                download_count=F('download_count') + download_count
-            )
+        AssetBlob.objects.bulk_update(asset_blobs, ['download_count'], batch_size=1_000)