diff --git a/src/main/notebooks/performance.dashboard.scala b/src/main/notebooks/performance.dashboard.scala
new file mode 100644
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--- /dev/null
+++ b/src/main/notebooks/performance.dashboard.scala
@@ -0,0 +1,255 @@
+// Databricks notebook source exported at Fri, 11 Sep 2015 06:27:08 UTC
+import com.databricks.spark.sql.perf._
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.expressions._
+import org.apache.spark.sql.types._
+
+sqlContext.read.load("/databricks/spark/sql/sqlPerformanceCompact").registerTempTable("sqlPerformanceCompact")
+
+val timestampWindow = Window.partitionBy("sparkVersion").orderBy($"timestamp".desc)
+
+val normalizeVersion = udf((v: String) => v.stripSuffix("-SNAPSHOT"))
+
+class RecentRuns(prefix: String, inputFunction: DataFrame => DataFrame) {
+  val inputData = table("sqlPerformanceCompact") 
+    .where($"tags.runtype" === "daily")
+    .withColumn("sparkVersion", normalizeVersion($"configuration.sparkVersion"))
+    .withColumn("startTime", from_unixtime($"timestamp" / 1000))
+    .withColumn("database", $"tags.database")
+    .withColumn("jobname", $"tags.jobname")
+  
+  val recentRuns = inputFunction(inputData)
+    .withColumn("runId", denseRank().over(timestampWindow))
+    .withColumn("runId", -$"runId")
+    .filter($"runId" >= -10) 
+
+  val baseData = recentRuns
+    .withColumn("result", explode($"results"))
+    .withColumn("day", concat(month($"startTime"), lit("-"), dayofmonth($"startTime"))) 
+    .withColumn("runtimeSeconds", runtime / 1000)
+    .withColumn("queryName", $"result.name")
+
+  baseData.registerTempTable(s"${prefix}_baseData")
+
+  val smoothed = baseData
+    .where($"iteration" !== 1)
+    .groupBy("runId", "sparkVersion", "database", "queryName")
+    .agg(callUDF("percentile", $"runtimeSeconds".cast("long"), lit(0.5)).as('medianRuntimeSeconds))
+    .orderBy($"runId", $"sparkVersion")
+
+  smoothed.registerTempTable(s"${prefix}_smoothed")
+}
+
+val tpcds1500 = new RecentRuns("tpcds1500", _.filter($"database" === "tpcds1500"))
+
+// COMMAND ----------
+
+display(tpcds1500.recentRuns.select($"runId", $"sparkVersion", $"startTime", $"timestamp").distinct)
+
+// COMMAND ----------
+
+class GeometricMean extends org.apache.spark.sql.expressions.UserDefinedAggregateFunction {
+  def bufferSchema: StructType = StructType(
+    StructField("count", LongType) ::
+    StructField("product", DoubleType) :: Nil
+  )
+  
+  def dataType: DataType = DoubleType
+  
+  def deterministic: Boolean = true
+  
+  def evaluate(buffer: org.apache.spark.sql.Row): Any = {
+    math.pow(buffer.getDouble(1), 1.toDouble / buffer.getLong(0))
+  }
+  
+  def initialize(buffer: org.apache.spark.sql.expressions.MutableAggregationBuffer): Unit = {
+    buffer(0) = 0L
+    buffer(1) = 1.0
+  }
+  
+  def inputSchema: org.apache.spark.sql.types.StructType = 
+    StructType(StructField("value", DoubleType) :: Nil)
+  
+  def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
+    buffer1(0) = buffer1.getAs[Long](0) + buffer2.getAs[Long](0)
+    buffer1(1) = buffer1.getAs[Double](1) * buffer2.getAs[Double](1)
+  }
+  
+  def update(buffer: MutableAggregationBuffer,input: Row): Unit = {
+    buffer(0) = buffer.getAs[Long](0) + 1
+    buffer(1) = buffer.getAs[Double](1) * input.getAs[Double](0)
+  }
+}
+
+val gm = new GeometricMean
+sqlContext.udf.register("gm", gm)
+
+// COMMAND ----------
+
+// MAGIC %python spark_versions = ["1.6.0", "1.5.0", "1.4.1"]
+// MAGIC 
+// MAGIC def plot_single(df, column, height = 30):
+// MAGIC   for v in spark_versions:
+// MAGIC     runtimes = df[df['sparkVersion'] == v][column]
+// MAGIC     padded = ([0.0] * (10 - len(runtimes))) + list(runtimes)
+// MAGIC     plt.plot(range(10), padded)
+// MAGIC 
+// MAGIC   plt.xticks(range(10), list(df[df['sparkVersion'] == v]['runId']))
+// MAGIC 
+// MAGIC   plt.legend(spark_versions, loc='lower left')
+// MAGIC   plt.ylim([0,height])
+
+// COMMAND ----------
+
+// MAGIC %md ## TPCDS - 20 Queries, Total Runtime (Smoothed to reduce outliers)
+// MAGIC  -  time for each query = median of 4 iterations
+// MAGIC  - dropping 1 warmup iteration
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC import matplotlib.pyplot as plt
+// MAGIC import numpy as np
+// MAGIC from pyspark.sql.functions import *
+// MAGIC 
+// MAGIC df = table("tpcds1500_smoothed") \
+// MAGIC   .groupBy("runId", "sparkVersion") \
+// MAGIC   .agg(sum("medianRuntimeSeconds").alias("totalRunTime")) \
+// MAGIC   .toPandas()
+// MAGIC 
+// MAGIC fig = plt.figure(figsize=(12, 2))
+// MAGIC plot_single(df, "totalRunTime", height=1000)
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+org.apache.spark.SPARK_VERSION
+
+// COMMAND ----------
+
+// MAGIC %python df['sparkVersion'].unique()
+
+// COMMAND ----------
+
+// MAGIC %md ## TPCDS - 20 Queries Geometric Mean
+// MAGIC  - time for each query = median of 4 iterations
+// MAGIC  - dropping 1 warmup iteration
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC import matplotlib.pyplot as plt
+// MAGIC from pyspark.sql.functions import *
+// MAGIC 
+// MAGIC df = table("tpcds1500_smoothed").groupBy("runId", "sparkVersion").agg(expr("gm(medianRuntimeSeconds) AS score")).toPandas()
+// MAGIC 
+// MAGIC fig = plt.figure(figsize=(12, 2))
+// MAGIC plot_single(df, "score")
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC import matplotlib.pyplot as plt
+// MAGIC from math import ceil, floor
+// MAGIC 
+// MAGIC def plot_queries(df, queries):
+// MAGIC   num_queries = len(queries)
+// MAGIC   rows = int(ceil(num_queries / 2))
+// MAGIC 
+// MAGIC   for idx, name in enumerate(queries):
+// MAGIC     data = df[df['queryName'] == name]
+// MAGIC     plt.subplot(rows, 2 , idx)
+// MAGIC     plt.title(name)
+// MAGIC 
+// MAGIC 
+// MAGIC     plot_single(data, 'medianRuntimeSeconds', 200)
+
+// COMMAND ----------
+
+// MAGIC %md # Interactive Queries
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC fig = plt.figure(figsize=(15, 20))
+// MAGIC plot_queries(table("tpcds1500_smoothed").toPandas(), ["q19", "q42", "q52", "q55", "q63", "q68", "q73", "q98"])
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %md #Reporting Queries
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC fig = plt.figure(figsize=(15, 20))
+// MAGIC plot_queries(table("tpcds1500_smoothed").toPandas(), ["q3","q7","q27","q43", "q53", "q89"])
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %md # Deep Analytics
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC fig = plt.figure(figsize=(15, 20))
+// MAGIC plot_queries(table("tpcds1500_smoothed").toPandas(), ["q34", "q46", "q59", "q65",  "q79", "ss_max"])
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+val joins = new RecentRuns("joins", _.filter($"jobname" === "joins.daily"))
+
+// COMMAND ----------
+
+display(table("joins_smoothed").groupBy("runId", "sparkVersion").agg(sum("medianRuntimeSeconds").alias("runtime")))
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC df = table("joins_smoothed").groupBy("runId", "sparkVersion").agg(sum("medianRuntimeSeconds").alias("runtime")).toPandas()
+// MAGIC 
+// MAGIC fig = plt.figure(figsize=(12, 2))
+// MAGIC plot_single(df, "runtime", 2500)
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC df = table("joins_smoothed").groupBy("runId", "sparkVersion").agg(expr("gm(medianRuntimeSeconds) AS score")).toPandas()
+// MAGIC 
+// MAGIC fig = plt.figure(figsize=(12, 2))
+// MAGIC plot_single(df, "score", 200)
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %python
+// MAGIC fig = plt.figure(figsize=(15, 20))
+// MAGIC df = table("joins_smoothed").orderBy("queryName").toPandas()
+// MAGIC plot_queries(df, list(df['queryName'].unique()))
+// MAGIC display(fig)
+
+// COMMAND ----------
+
+// MAGIC %md # Query Plan Breakdown
+
+// COMMAND ----------
+
+val result = tpcds1500.baseData.where($"queryName" === getArgument("queryName", "ss_max")).groupBy("sparkVersion").agg(first($"result.queryExecution")).orderBy("sparkVersion").collect().map {
+  case Row(version: String, queryExecution: String) =>
+    s"""
+     |<h1>$version</h1>
+     |<pre>$queryExecution</pre>
+     """.stripMargin
+
+}.mkString("<br/>")
+
+displayHTML(result)
+
+// COMMAND ----------
+