diff --git a/labs/GSP1298/HASHNODE.md b/labs/GSP1298/HASHNODE.md
new file mode 100644
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+# Stream data with pipelines - Esports Use case - GSP1298
+
+## Overview
+
+In this Google Skills lab, a Python simulator publishes esports match events to Pub/Sub, Apache Beam processes them on Dataflow, and BigQuery stores raw events and player/team score updates. Two views select each competitor's latest score and rank the leaderboard.
+
+The official lab contains **five Tasks and four checkpoints** (30/20/30/20 points). The [original architecture diagrams](https://www.skills.google/focuses/132337?parent=catalog) are linked, not copied, because reuse/embedding rights are unverified.
+
+### What you'll do
+
+- Identify the lab project, region, and existing Cloud Storage bucket.
+- Create a Pub/Sub topic/subscription and BigQuery dataset.
+- Retrieve and configure two official Python files.
+- Run a synthetic event producer and Dataflow streaming job.
+- Create and query live player/team leaderboard views.
+
+## Setup and requirements
+
+### Before you click the Start Lab button
+
+Open [GSP1298 on Google Skills](https://www.skills.google/focuses/132337?parent=catalog). Start the timed lab only when ready, use a private browser, and sign in with the **temporary student account** from the lab panel. Do not use a personal project or publish student credentials.
+
+The lab provisions a Cloud Storage bucket and assigns a region. Use the provided values, not guessed defaults. A streaming Dataflow job can incur usage outside the timed lab.
+
+### Activate Cloud Shell
+
+Activate Cloud Shell, authorize the lab account, and inspect the project, region, and bucket:
+
+```bash
+gcloud auth list
+export PROJECT_ID="$(gcloud config get-value project)"
+export REGION="$(gcloud config get-value compute/region)"
+export BUCKET_NAME="${PROJECT_ID}-bucket"
+printf 'Project=%s Region=%s Bucket=%s\n' "$PROJECT_ID" "$REGION" "$BUCKET_NAME"
+gcloud storage buckets describe "gs://$BUCKET_NAME"
+```
+
+If the configured region is unset, use the region displayed in the lab panel. The bucket must already exist; do not create a substitute.
+
+## Task 1. Configure environment variables in Cloud Shell
+
+Keep `PROJECT_ID`, `REGION`, and `BUCKET_NAME` exported for all commands. New Cloud Shell tabs may require exporting them again.
+
+```bash
+echo "$PROJECT_ID"
+echo "$REGION"
+echo "$BUCKET_NAME"
+gcloud config get-value project
+```
+
+## Task 2. Create the cloud resources
+
+Create the exact Pub/Sub topic and subscription, and a BigQuery dataset in the `US` multiregion as specified by Google Skills:
+
+```bash
+gcloud services enable pubsub.googleapis.com bigquery.googleapis.com dataflow.googleapis.com
+gcloud pubsub topics create esports_events_topic
+gcloud pubsub subscriptions create esports_events_topic-sub --topic=esports_events_topic
+bq --location=US mk --dataset esports_analytics
+```
+
+Check the resources before repeating any creation command:
+
+```bash
+gcloud pubsub topics describe esports_events_topic
+gcloud pubsub subscriptions describe esports_events_topic-sub
+bq show "$PROJECT_ID:esports_analytics"
+```
+
+### Test completed task
+
+Click **Check my progress** for **Create the cloud resources** (30 points). Confirm the subscription belongs to the expected topic.
+
+## Task 3. Retrieve the Python files and configure them
+
+Download the two official source files, which are provided as public Google Cloud Storage objects. The simulator generates match-start, elimination and match-end messages; the pipeline reads them from Pub/Sub and writes BigQuery score tables.
+
+```bash
+mkdir -p "$HOME/esports"
+cd "$HOME/esports"
+curl -fL --retry 3 -o esports-simulation.py \
+  https://storage.googleapis.com/spls/gsp1298/esports-simulation.py
+curl -fL --retry 3 -o esports-pipeline.py \
+  https://storage.googleapis.com/spls/gsp1298/esports-pipeline.py
+```
+
+Update the top-level `PROJECT_ID` assignments in both files and the `REGION` and `GCS_TEMP_LOCATION` assignments in the pipeline. This Python helper replaces only the expected assignments and fails if the upstream file structure differs:
+
+```bash
+python3 - "$HOME/esports" "$PROJECT_ID" "$REGION" "$BUCKET_NAME" <<'PY'
+from pathlib import Path
+import re
+import sys
+folder, project, region, bucket = sys.argv[1:]
+for name in ("esports-simulation.py", "esports-pipeline.py"):
+    path = Path(folder) / name
+    source = path.read_text()
+    fields = {"PROJECT_ID": project}
+    if name == "esports-pipeline.py":
+        fields.update({"REGION": region, "GCS_TEMP_LOCATION": f"gs://{bucket}/temp"})
+    for key, value in fields.items():
+        source, count = re.subn(
+            rf"^{key}\s*=\s*.*$", f"{key} = {value!r}", source, flags=re.MULTILINE
+        )
+        if count != 1:
+            raise SystemExit(f"Expected one {key} assignment in {name}, found {count}")
+    path.write_text(source)
+PY
+python3 -m py_compile esports-simulation.py esports-pipeline.py
+```
+
+Upload the configured files into the lab bucket under the required `esports/` prefix:
+
+```bash
+gcloud storage cp esports-simulation.py "gs://$BUCKET_NAME/esports/"
+gcloud storage cp esports-pipeline.py "gs://$BUCKET_NAME/esports/"
+gcloud storage ls "gs://$BUCKET_NAME/esports/"
+```
+
+### Test completed task
+
+Click **Check my progress** for **Retrieve the Python files and configure them** (20 points). These official Python files are runtime dependencies, not copied into the ePlus repository.
+
+## Task 4. Generate synthetic data and run the pipeline
+
+### Run the simulator
+
+Create a Python virtual environment, install the required Pub/Sub and Beam packages, and start the simulator. It runs continuously; keep the terminal open.
+
+```bash
+cd "$HOME/esports"
+python3 -m venv df-env
+source df-env/bin/activate
+python3 -m pip install google-cloud-pubsub 'apache-beam[gcp]'
+python3 esports-simulation.py
+```
+
+In the Google Cloud console, inspect Pub/Sub → Subscriptions → `esports_events_topic-sub` for incoming messages.
+
+### Set up Dataflow permissions
+
+The official lab documents a potential Dataflow service-agent IAM error. First inspect the project and pre-provisioned worker identity:
+
+```bash
+export PROJECT_NUMBER="$(gcloud projects describe "$PROJECT_ID" --format='value(projectNumber)')"
+export WORKER_SA="${PROJECT_NUMBER}-compute@developer.gserviceaccount.com"
+export DATAFLOW_AGENT="service-${PROJECT_NUMBER}@dataflow-service-producer-prod.iam.gserviceaccount.com"
+gcloud beta services identity create --service=dataflow.googleapis.com --project="$PROJECT_ID"
+```
+
+If permissions are missing, apply the scoped Dataflow roles in the authorized lab project:
+
+```bash
+gcloud projects add-iam-policy-binding "$PROJECT_ID" \
+  --member="serviceAccount:$DATAFLOW_AGENT" \
+  --role=roles/dataflow.serviceAgent --condition=None
+gcloud iam service-accounts add-iam-policy-binding "$WORKER_SA" \
+  --member="serviceAccount:$DATAFLOW_AGENT" \
+  --role=roles/iam.serviceAccountUser
+gcloud projects add-iam-policy-binding "$PROJECT_ID" \
+  --member="serviceAccount:$WORKER_SA" \
+  --role=roles/dataflow.worker --condition=None
+```
+
+A Cloud Storage **soft-delete warning** is described in the official lab. The automation does not disable bucket protection automatically; a warning alone is not a failure.
+
+### Launch Dataflow
+
+Open a second Cloud Shell tab, export the project, region, and bucket values again, activate the virtual environment, and submit the streaming job:
+
+```bash
+cd "$HOME/esports"
+source df-env/bin/activate
+python3 esports-pipeline.py \
+  --project="$PROJECT_ID" --region="$REGION" \
+  --runner=DataflowRunner --streaming \
+  --temp_location="gs://$BUCKET_NAME/temp" \
+  --job_name=esports-leaderboard-pipeline \
+  --worker_machine_type=e2-standard-2
+```
+
+Inspect Dataflow → Jobs. Wait for a job whose name starts with `esports-leaderboard` to become **Running**. Check worker logs and the BigQuery tables `raw_events`, `player_score_updates`, and `team_score_updates`. Time-window aggregation can delay the score tables by several minutes.
+
+### Test completed task
+
+Click **Check my progress** for **Generate synthetic data and run the pipeline** (30 points). Keep the streaming job active until grading is finished.
+
+## Task 5. Verify the results in BigQuery
+
+Create the player leaderboard view, selecting each player's latest score before ranking:
+
+```sql
+CREATE OR REPLACE VIEW esports_analytics.player_leaderboard_live AS
+SELECT RANK() OVER (ORDER BY total_score DESC) AS rank,
+       player_id, total_score, last_updated
+FROM (
+  SELECT player_id, total_score, last_updated,
+         ROW_NUMBER() OVER (PARTITION BY player_id ORDER BY last_updated DESC) AS rn
+  FROM esports_analytics.player_score_updates
+)
+WHERE rn = 1;
+```
+
+Create the team leaderboard view in the same way:
+
+```sql
+CREATE OR REPLACE VIEW esports_analytics.team_leaderboard_live AS
+SELECT RANK() OVER (ORDER BY total_wins DESC) AS rank,
+       team_id, total_wins, last_updated
+FROM (
+  SELECT team_id, total_wins, last_updated,
+         ROW_NUMBER() OVER (PARTITION BY team_id ORDER BY last_updated DESC) AS rn
+  FROM esports_analytics.team_score_updates
+)
+WHERE rn = 1;
+```
+
+Query both views with BigQuery Studio or the CLI:
+
+```bash
+bq query --use_legacy_sql=false \
+  'SELECT * FROM esports_analytics.player_leaderboard_live ORDER BY rank LIMIT 10'
+bq query --use_legacy_sql=false \
+  'SELECT * FROM esports_analytics.team_leaderboard_live ORDER BY rank LIMIT 10'
+```
+
+The leading player and team vary with the synthetic events. Do not hardcode the result.
+
+### Test completed task
+
+Click **Check my progress** for **Verify the results in BigQuery** (20 points). The checkpoints total 100 points; grader success is not asserted.
+
+---
+
+## Solution of Lab
+
+### Quick
+
+Run the following two commands in the **authorized temporary lab Cloud Shell**. The URL deliberately targets `main`; it will only work **after the Draft PR is merged**. The script leaves streaming jobs running for grading.
+
+```bash
+curl -LO https://raw.githubusercontent.com/ePlus-DEV/storage/refs/heads/main/labs/GSP1298/lab.sh
+source lab.sh
+```
+
+The script discovers the project and region, uses the existing lab bucket, downloads and syntax-checks the official Python files, provisions the resources, launches the pipeline, and creates the views. It does not press Google Skills' grader buttons.
+
+### Manual
+
+Chưa tìm thấy video YouTube hướng dẫn thủ công đã xác minh cho lab này.
+
+Follow Tasks 1–5 above or the [official lab](https://www.skills.google/focuses/132337?parent=catalog).
+
+## Sources and verification status
+
+- [Google Skills GSP1298 official lab](https://www.skills.google/focuses/132337?parent=catalog), last tested October 15, 2026.
+- [Official simulator source](https://storage.googleapis.com/spls/gsp1298/esports-simulation.py).
+- [Official Dataflow pipeline source](https://storage.googleapis.com/spls/gsp1298/esports-pipeline.py).
+- [Dataflow security and permissions](https://cloud.google.com/dataflow/docs/concepts/security-and-permissions).
+- [BigQuery views documentation](https://cloud.google.com/bigquery/docs/views-intro).
+
+**static validated only; runtime/grader unverified.** The official Python source URLs were identified but not executed in an authorized lab. Python syntax is checked at runtime, while Bash/ShellCheck require latest-commit GitHub CI. IAM propagation, Dataflow worker readiness and streaming table creation vary by session. No private credentials are embedded. This Markdown is committed for review, not published to Hashnode.
+
+Copyright (c) 2026 ePlus.DEV | Unlicense — original automation and explanatory prose.
diff --git a/labs/GSP1298/lab.sh b/labs/GSP1298/lab.sh
new file mode 100644
index 0000000..bc5a07c
--- /dev/null
+++ b/labs/GSP1298/lab.sh
@@ -0,0 +1,179 @@
+#!/usr/bin/env bash
+# GSP1298 — Stream data with pipelines - Esports Use case
+# Copyright (c) 2026 ePlus.DEV | Unlicense
+# static validated only; runtime/grader unverified
+set -Eeuo pipefail
+umask 077
+if [[ -n "${NO_COLOR+x}" ]]; then C='' G='' Y='' R='';
+elif [[ -t 1 || "${FORCE_COLOR:-0}" == 1 ]]; then
+  C=$'\033[36m'; G=$'\033[32m'; Y=$'\033[33m'; R=$'\033[0m'
+else C='' G='' Y='' R=''; fi
+info(){ printf '%s[INFO]%s %s\n' "$C" "$R" "$*"; }
+ok(){ printf '%s[OK]%s %s\n' "$G" "$R" "$*"; }
+warn(){ printf '%s[WARN]%s %s\n' "$Y" "$R" "$*" >&2; }
+die(){ printf '[ERROR] %s\n' "$*" >&2; exit 1; }
+trap 'warn "Stopped near line $LINENO; inspect errors. No grader PASS is implied."' ERR
+printf '%sePlus.DEV – Starting Execution%s\n' "$C" "$R"
+printf 'Copyright (c) 2026 ePlus.DEV | Unlicense\n'
+for cmd in gcloud bq curl python3 jq; do command -v "$cmd" >/dev/null || die "Missing $cmd"; done
+PROJECT_ID="${PROJECT_ID:-$(gcloud config get-value project 2>/dev/null)}"
+[[ -n "$PROJECT_ID" && "$PROJECT_ID" != '(unset)' ]] || die 'Choose the temporary lab project.'
+REGION="${REGION:-$(gcloud config get-value compute/region 2>/dev/null)}"
+if [[ -z "$REGION" || "$REGION" == '(unset)' ]]; then
+  ZONE="$(gcloud config get-value compute/zone 2>/dev/null)"
+  [[ -n "$ZONE" && "$ZONE" != '(unset)' ]] || die 'Set REGION to the lab-assigned region.'
+  REGION="${ZONE%-*}"
+fi
+[[ "$REGION" =~ ^[a-z]+-[a-z0-9]+[0-9]$ ]] || die 'Invalid region.'
+BUCKET_NAME="${BUCKET_NAME:-${PROJECT_ID}-bucket}"
+BUCKET_NAME="${BUCKET_NAME#gs://}"; BUCKET_NAME="${BUCKET_NAME%/}"
+gcloud storage buckets describe "gs://$BUCKET_NAME" --project="$PROJECT_ID" >/dev/null ||
+  die 'Expected pre-provisioned lab bucket is missing. Set BUCKET_NAME from the lab panel.'
+WORKDIR="${WORKDIR:-$HOME/esports}"
+mkdir -p "$WORKDIR"
+info "Project=$PROJECT_ID Region=$REGION Bucket=gs://$BUCKET_NAME"
+retry(){ local count="$1" delay="$2" n; shift 2
+  for ((n=1;n<=count;n++)); do
+    if "$@"; then return 0; fi
+    if ((n<count)); then warn "Retry $n/$count"; sleep "$delay"; fi
+  done; return 1; }
+
+info 'Task 1: configure project, region, and existing lab bucket'
+gcloud config get-value project >/dev/null
+info 'Task 2: create Pub/Sub and BigQuery resources without duplicates'
+gcloud services enable pubsub.googleapis.com bigquery.googleapis.com dataflow.googleapis.com --project="$PROJECT_ID" --quiet
+gcloud pubsub topics describe esports_events_topic --project="$PROJECT_ID" >/dev/null 2>&1 ||
+  gcloud pubsub topics create esports_events_topic --project="$PROJECT_ID"
+gcloud pubsub subscriptions describe esports_events_topic-sub --project="$PROJECT_ID" >/dev/null 2>&1 ||
+  gcloud pubsub subscriptions create esports_events_topic-sub --project="$PROJECT_ID" --topic=esports_events_topic
+bq show --project_id="$PROJECT_ID" --format=none "$PROJECT_ID:esports_analytics" >/dev/null 2>&1 ||
+  bq --project_id="$PROJECT_ID" --location=US mk --dataset esports_analytics
+gcloud pubsub topics describe esports_events_topic --project="$PROJECT_ID" >/dev/null
+gcloud pubsub subscriptions describe esports_events_topic-sub --project="$PROJECT_ID" >/dev/null
+bq show --project_id="$PROJECT_ID" "$PROJECT_ID:esports_analytics" >/dev/null
+ok 'Checkpoint: Create the cloud resources (30 points)'
+
+info 'Task 3: fetch official Python source and configure all dynamic values'
+for name in esports-simulation.py esports-pipeline.py; do
+  if [[ ! -e "$WORKDIR/$name" ]]; then
+    retry 3 5 curl -fL --retry 3 "https://storage.googleapis.com/spls/gsp1298/$name" \
+      -o "$WORKDIR/$name" || die "Unable to retrieve official $name"
+  fi
+  [[ -s "$WORKDIR/$name" ]] || die "Empty $name"
+done
+python3 - "$WORKDIR" "$PROJECT_ID" "$REGION" "$BUCKET_NAME" <<'PY'
+from pathlib import Path
+import re
+import sys
+directory, project, region, bucket = sys.argv[1:]
+for name in ("esports-simulation.py", "esports-pipeline.py"):
+    path = Path(directory) / name
+    text = path.read_text(encoding="utf-8")
+    fields = {"PROJECT_ID": project}
+    if name == "esports-pipeline.py":
+        fields.update({"REGION": region, "GCS_TEMP_LOCATION": f"gs://{bucket}/temp"})
+    for key, value in fields.items():
+        pattern = rf"^{re.escape(key)}\s*=\s*.*$"
+        text, count = re.subn(pattern, f"{key} = {value!r}", text, flags=re.MULTILINE)
+        if count != 1:
+            raise SystemExit(f"Expected exactly one {key} assignment in {name}, got {count}")
+    path.write_text(text, encoding="utf-8")
+PY
+python3 -m py_compile "$WORKDIR/esports-simulation.py" "$WORKDIR/esports-pipeline.py"
+gcloud storage cp "$WORKDIR/esports-simulation.py" "gs://$BUCKET_NAME/esports/esports-simulation.py" --quiet
+gcloud storage cp "$WORKDIR/esports-pipeline.py" "gs://$BUCKET_NAME/esports/esports-pipeline.py" --quiet
+gcloud storage objects describe "gs://$BUCKET_NAME/esports/esports-simulation.py" >/dev/null
+gcloud storage objects describe "gs://$BUCKET_NAME/esports/esports-pipeline.py" >/dev/null
+ok 'Checkpoint: Retrieve the Python files and configure them (20 points)'
+
+info 'Task 4: install isolated dependencies and launch bounded background processes'
+if [[ ! -x "$WORKDIR/df-env/bin/python" ]]; then python3 -m venv "$WORKDIR/df-env"; fi
+retry 3 8 "$WORKDIR/df-env/bin/python" -m pip install --disable-pip-version-check \
+  google-cloud-pubsub 'apache-beam[gcp]' ||
+  die 'Unable to install required Python dependencies.'
+if [[ -s "$WORKDIR/simulator.pid" ]] &&
+   kill -0 "$(cat "$WORKDIR/simulator.pid")" 2>/dev/null; then
+  info 'Existing simulator process is running.'
+else
+  nohup "$WORKDIR/df-env/bin/python" "$WORKDIR/esports-simulation.py" \
+    > "$WORKDIR/simulator.log" 2>&1 < /dev/null &
+  echo "$!" > "$WORKDIR/simulator.pid"
+fi
+sleep 10
+kill -0 "$(cat "$WORKDIR/simulator.pid")" 2>/dev/null ||
+  die 'Simulator exited unexpectedly; inspect simulator.log.'
+
+PROJECT_NUMBER="$(gcloud projects describe "$PROJECT_ID" --format='value(projectNumber)')"
+[[ "$PROJECT_NUMBER" =~ ^[0-9]+$ ]] || die 'Could not discover project number.'
+WORKER_SA="${PROJECT_NUMBER}-compute@developer.gserviceaccount.com"
+DATAFLOW_AGENT="service-${PROJECT_NUMBER}@dataflow-service-producer-prod.iam.gserviceaccount.com"
+gcloud beta services identity create --service=dataflow.googleapis.com --project="$PROJECT_ID" >/dev/null
+# Grant the Dataflow service identity only the documented agent role.
+gcloud projects add-iam-policy-binding "$PROJECT_ID" \
+  --member="serviceAccount:$DATAFLOW_AGENT" \
+  --role=roles/dataflow.serviceAgent --condition=None --quiet >/dev/null
+# Let the Dataflow service agent impersonate the pre-provisioned worker identity.
+gcloud iam service-accounts add-iam-policy-binding "$WORKER_SA" \
+  --project="$PROJECT_ID" --member="serviceAccount:$DATAFLOW_AGENT" \
+  --role=roles/iam.serviceAccountUser --quiet >/dev/null
+gcloud projects add-iam-policy-binding "$PROJECT_ID" \
+  --member="serviceAccount:$WORKER_SA" \
+  --role=roles/dataflow.worker --condition=None --quiet >/dev/null
+
+if [[ -s "$WORKDIR/pipeline.pid" ]] &&
+   kill -0 "$(cat "$WORKDIR/pipeline.pid")" 2>/dev/null; then
+  info 'Existing Dataflow launcher is running.'
+elif gcloud dataflow jobs list --project="$PROJECT_ID" --region="$REGION" --format=json |
+     jq -e '.[] | select((.name | startswith("esports-leaderboard")) and
+       (.state == "Running" or .state == "JOB_STATE_RUNNING"))' >/dev/null; then
+  info 'Existing esports Dataflow job is running.'
+else
+  nohup "$WORKDIR/df-env/bin/python" "$WORKDIR/esports-pipeline.py" \
+    --project="$PROJECT_ID" --region="$REGION" --runner=DataflowRunner --streaming \
+    --temp_location="gs://$BUCKET_NAME/temp" --job_name=esports-leaderboard-pipeline \
+    --worker_machine_type=e2-standard-2 > "$WORKDIR/pipeline.log" 2>&1 < /dev/null &
+  echo "$!" > "$WORKDIR/pipeline.pid"
+fi
+running=0
+for ((i=1;i<=24;i++)); do
+  if gcloud dataflow jobs list --project="$PROJECT_ID" --region="$REGION" --format=json |
+     jq -e '.[] | select((.name | startswith("esports-leaderboard")) and
+       (.state == "Running" or .state == "JOB_STATE_RUNNING"))' >/dev/null; then
+    running=1; break
+  fi
+  sleep 15
+done
+(( running == 1 )) || die 'Dataflow job did not reach RUNNING in 6 minutes. Inspect pipeline.log.'
+ok 'Checkpoint: Generate synthetic data and run the pipeline (30 points)'
+
+info 'Task 5: wait for streaming tables, then create the two official leaderboard views'
+ready=0
+for ((i=1;i<=24;i++)); do
+  if bq show "$PROJECT_ID:esports_analytics.player_score_updates" >/dev/null 2>&1 &&
+     bq show "$PROJECT_ID:esports_analytics.team_score_updates" >/dev/null 2>&1; then
+    ready=1; break
+  fi
+  sleep 15
+done
+(( ready == 1 )) || die 'Expected streaming score tables not ready; inspect Dataflow logs.'
+bq query --project_id="$PROJECT_ID" --use_legacy_sql=false --format=none \
+"CREATE OR REPLACE VIEW `$PROJECT_ID.esports_analytics.player_leaderboard_live` AS
+ SELECT RANK() OVER (ORDER BY total_score DESC) AS rank, player_id, total_score, last_updated
+ FROM (SELECT player_id, total_score, last_updated,
+ ROW_NUMBER() OVER (PARTITION BY player_id ORDER BY last_updated DESC) AS rn
+ FROM `$PROJECT_ID.esports_analytics.player_score_updates`) WHERE rn=1;"
+bq query --project_id="$PROJECT_ID" --use_legacy_sql=false --format=none \
+"CREATE OR REPLACE VIEW `$PROJECT_ID.esports_analytics.team_leaderboard_live` AS
+ SELECT RANK() OVER (ORDER BY total_wins DESC) AS rank, team_id, total_wins, last_updated
+ FROM (SELECT team_id, total_wins, last_updated,
+ ROW_NUMBER() OVER (PARTITION BY team_id ORDER BY last_updated DESC) AS rn
+ FROM `$PROJECT_ID.esports_analytics.team_score_updates`) WHERE rn=1;"
+bq show "$PROJECT_ID:esports_analytics.player_leaderboard_live" >/dev/null
+bq show "$PROJECT_ID:esports_analytics.team_leaderboard_live" >/dev/null
+bq query --project_id="$PROJECT_ID" --use_legacy_sql=false \
+  "SELECT * FROM `$PROJECT_ID.esports_analytics.player_leaderboard_live` ORDER BY rank LIMIT 10"
+bq query --project_id="$PROJECT_ID" --use_legacy_sql=false \
+  "SELECT * FROM `$PROJECT_ID.esports_analytics.team_leaderboard_live` ORDER BY rank LIMIT 10"
+ok 'Checkpoint: Verify the results in BigQuery (20 points)'
+info 'Streaming jobs remain running for the timed lab. Do not cancel before grader review.'
+info 'static validated only; runtime/grader unverified'