diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py
index ca3ef325..fc78df25 100644
--- a/benchmarks/benchmarks.py
+++ b/benchmarks/benchmarks.py
@@ -3,14 +3,62 @@
 For more information on writing benchmarks:
 https://asv.readthedocs.io/en/stable/writing_benchmarks.html."""
 
-import example_benchmarks
+#import example_benchmarks
+import numpy as np
+import pandas as pd
+from tape.ensemble import Ensemble
 
 
-def time_computation():
-    """Time computations are prefixed with 'time'."""
-    example_benchmarks.runtime_computation()
+#def time_computation():
+#    """Time computations are prefixed with 'time'."""
+#    example_benchmarks.runtime_computation()
 
 
-def mem_list():
-    """Memory computations are prefixed with 'mem' or 'peakmem'."""
-    return example_benchmarks.memory_computation()
+#def mem_list():
+#    """Memory computations are prefixed with 'mem' or 'peakmem'."""
+#    return example_benchmarks.memory_computation()
+
+
+def time_basic_workflow():
+    np.random.seed(1)
+
+    # Generate 10 astronomical objects
+    n_obj = 10
+    ids = 8000 + np.arange(n_obj)
+    names = ids.astype(str)
+    object_table = pd.DataFrame(
+        {
+            "id": ids,
+            "name": names,
+            "ddf_bool": np.random.randint(0, 2, n_obj), # 0 if from deep drilling field, 1 otherwise
+            "libid_cadence": np.random.randint(1, 130, n_obj),
+        }
+    )
+
+    # Create 1000 lightcurves with 100 measurements each
+    lc_len = 100
+    num_points = 1000
+    all_bands = np.array(["r", "g", "b", "i"])
+    source_table = pd.DataFrame(
+        {
+            "id": 8000 + (np.arange(num_points) % n_obj),
+            "time": np.arange(num_points),
+            "flux":  np.random.random_sample(size=num_points)*10,
+            "band": np.repeat(all_bands, num_points / len(all_bands)),
+            "error": np.random.random_sample(size=num_points),
+            "count": np.arange(num_points),
+        },
+    )
+
+    ens = Ensemble()  # initialize an ensemble object
+
+    # Read in the generated lightcurve data
+    ens.from_pandas(
+        source_frame=source_table,
+        object_frame=object_table,
+        id_col="id",
+        time_col="time",
+        flux_col="flux",
+        err_col="error",
+        band_col="band",
+        npartitions=1)