diff --git a/README.md b/README.md
index 6e259d0..2abba71 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,14 @@
 
 This is a native connector for reading and writing MongoDB collections
 directly from Apache Spark. In Spark, the data from MongoDB is represented as an
-RDD[MongoDBObject].
+RDD[MongoDBObject]. Starting with Release 0.5.0, you can also use NSMC through Spark SQL
+by registering a MongpDB collection as a temporary table. 
 
-Fore more details about this project, check this [blog post](http://www.river-of-bytes.com/2015/01/nsmc-native-mongodb-connector-for.html).
+Fore more details about this project, check the following blog posts:
+
+[NSMC: A Native MongoDB Connector for Apache Spark](http://www.river-of-bytes.com/2015/01/nsmc-native-mongodb-connector-for.html)
+
+[Spark SQL Integration for MongoDB](http://www.river-of-bytes.com/2015/02/spark-sql-integration-for-mongodb.html)
 
 # Current Limitations
 
@@ -13,7 +18,7 @@ Fore more details about this project, check this [blog post](http://www.river-of
 - Only tested with the following configurations:
  - MongoDB: 2.6
  - Scala: 2.10
- - Spark: 1.1.0
+ - Spark: 1.3.0
  - Casbah 2.7
 - Not tested with MongoDB's hash-based partitioning
 - Known to leak resources quite badly
@@ -24,7 +29,7 @@ Fore more details about this project, check this [blog post](http://www.river-of
 
 # Release Status
 
-A pre-release alpha version (0.3.0) is available.
+A pre-release alpha version (0.5.0) is available.
 
 # Licensing
 
@@ -32,30 +37,42 @@ See the top-level LICENSE file
 
 # Getting Started
 
+Perhaps the **easiest way to get started** is to use the [companion example project](https://github.com/spirom/spark-mongodb-examples). It contains an appropriately configured **sbt** project, together with sample code for using NSMC from both core Spark and Spark SQL. Be sure to check out that project's README to to make sure you're using the right branch for the NSMC and Apache Spark versions you want to use. 
+
+# **sbt** configuration
+
 Add the following to your build.sbt for the latest _stable_ release:
 
     scalaVersion := "2.10.4" // any 2.10 is OK -- support for 2.11 coming soon
 
-    libraryDependencies += "org.apache.spark" %% "spark-core" % "1.1.0"
+    libraryDependencies += "org.apache.spark" %% "spark-core" % "1.3.0"
 
-    libraryDependencies += "com.github.spirom" %% "spark-mongodb-connector" % "0.3.0"
+    libraryDependencies += "org.apache.spark" %% "spark-sql" % "1.3.0"
 
-To use the latest snapshot release:
+    libraryDependencies += "com.github.spirom" %% "spark-mongodb-connector" % "0.5.0"
+
+# Configuration
 
-    scalaVersion := "2.10.4" // any 2.10 is OK -- support for 2.11 coming soon
 
-    resolvers +=
-        "SonatypeOSSSnapshots" at "https://oss.sonatype.org/content/repositories/snapshots"
+| Setting | Meaning | Units | Default |
+|---------|---------|-------|---------|
+| nsmc.connection.host | MongoDB host or IP address | | localhost |
+| nsmc.connection.port | MongoDB port | | 27017 |
+| nsmc.user | MongoDB user name | | no authentication | 
+| nsmc.password | MongoDB password | | no authentication |
+| nsmc.split.indexed.collections | Should indexed collections be partitioned using MongoDB's [internal] splitVector command? | boolean | false |
+| nsmc.split.chunk.size | Maximum chunk size, in megabytes, passed to MongoDB's splitVector command, if used. | MB | 4 |
+| nsmc.partition.on.shard.chunks | Should collections that are already sharded in MongoDB retain this as their partitioning in Spark? If not, the entire collection will be read as a single Spark partition. | boolean | false |
+| nsmc.direct.to.shards | If sharding of collections is being observed, should the mongos server be bypassed? (Don't do this unless you understand MongoDB really well, or you may obtain incorrect results -- if MongoDB is rebalancing the shards when your query executes.) | boolean | false |
 
-    libraryDependencies += "org.apache.spark" %% "spark-core" % "1.1.0"
 
-    libraryDependencies += "com.github.spirom" %% "spark-mongodb-connector" % "0.2-SNAPSHOT"
+# Usage from core Spark
 
 In your code, you need to add:
 
     import nsmc._
 
-    import com.mongodb.casbah.commons.MongoDBObject
+    import com.mongodb.DBObject
 
 Then to actually read from a collection:
 
@@ -65,8 +82,30 @@ Then to actually read from a collection:
         .set("nsmc.connection.port", "myMongoPort")
         .set("nsmc.user", "yourUsernameHere")
         .set("nsmc.password", "yourPasswordHere")
-    val sc = newSparkContext(conf)
-    val data: MongoRDD[MongoDBObject] =
-        sc.mongoCollection[MongoDBObject]("myDB", "myCollection")
+    val sc = new SparkContext(conf)
+    val data: MongoRDD[DBObject] =
+        sc.mongoCollection[DBObject]("myDB", "myCollection")
+
+# Usage from Spark SQL
+
+Register a temporary table as follows
+
+    sqlContext.sql(
+        s"""
+        |CREATE TEMPORARY TABLE dataTable
+        |USING nsmc.sql.MongoRelationProvider
+        |OPTIONS (db 'myDB', collection 'myCollection')
+      """.stripMargin)
+
+Query the temporary table as follows
+
+    val data = sqlContext.sql("SELECT * FROM dataTable")
 
+# Summary of releases
 
+| NSMC Release | Status | Apache Spark Release | Scala Version | Features Added |
+---------------|--------|----------------------|---------------|----------------|
+| 0.5.0 | Alpha | 1.3.0 | 2.10 | Spark 1.3.0 |
+| 0.4.1 | Alpha | 1.2.0 | 2.10 | Filter and Projection push-down for SPark SQL |
+| 0.4.0 | Alpha | 1.2.0 | 2.10 | Spark 1.2.0 and Spark SQL Support |
+| 0.3.0 | Alpha | 1.1.0 | 2.10 | Initial Release |
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