diff --git a/README.md b/README.md
index 4a007ace..d5229e60 100644
--- a/README.md
+++ b/README.md
@@ -119,26 +119,11 @@ OPTIONS (dbtable 'my_table',
          url 'jdbc:redshift://host:port/db?user=username&password=pass');
 ```
 
-### Scala helper functions
-
-The <tt>com.databricks.spark.redshift</tt> package has some shortcuts if you're working directly
-from a Scala application and don't want to use the Data Sources API:
-
-```scala
-import com.databricks.spark.redshift._
-
-val sqlContext = new SQLContext(sc)
-
-val dataFrame = sqlContext.redshiftTable( ... )
-dataFrame.saveAsRedshiftTable( ... )
-```
-
 ### Hadoop InputFormat
 
 The library contains a Hadoop input format for Redshift tables unloaded with the ESCAPE option,
 which you may make direct use of as follows:
 
-Usage in Spark Core:
 ```scala
 import com.databricks.spark.redshift.RedshiftInputFormat
 
@@ -149,17 +134,6 @@ val records = sc.newAPIHadoopFile(
   classOf[Array[String]])
 ```
 
-Usage in Spark SQL:
-```scala
-import com.databricks.spark.redshift._
-
-// Call redshiftFile() that returns a SchemaRDD with all string columns.
-val records: DataFrame = sqlContext.redshiftFile(path, Seq("name", "age"))
-
-// Call redshiftFile() with the table schema.
-val records: DataFrame = sqlContext.redshiftFile(path, "name varchar(10) age integer")
-```
-
 ## Parameters
 
 The parameter map or <tt>OPTIONS</tt> provided in Spark SQL supports the following settings.
@@ -236,14 +210,6 @@ and use that as a temp location for this data.
     <td><tt>com.amazon.redshift.jdbc4.Driver</tt></td>
     <td>The class name of the JDBC driver to load before JDBC operations. Must be on classpath.</td>
  </tr>
- <tr>
-    <td><tt>overwrite</tt></td>
-    <td>No</td>
-    <td><tt>false</tt></td>
-    <td>
-If true, drop any existing data before writing new content. Only applies when using the Scala `saveAsRedshiftTable` function
-directly, as `SaveMode` will be preferred when using the Data Source API. See also <tt>usestagingtable</tt></td>
- </tr>
  <tr>
     <td><tt>diststyle</tt></td>
     <td>No</td>
diff --git a/src/main/scala/com/databricks/spark/redshift/Parameters.scala b/src/main/scala/com/databricks/spark/redshift/Parameters.scala
index 9b04fc5d..9f5caa7e 100644
--- a/src/main/scala/com/databricks/spark/redshift/Parameters.scala
+++ b/src/main/scala/com/databricks/spark/redshift/Parameters.scala
@@ -123,6 +123,7 @@ private[redshift] object Parameters extends Logging {
      *
      * Defaults to false.
      */
+    @deprecated("Use SaveMode instead", "0.5.0")
     def overwrite: Boolean = parameters("overwrite").toBoolean
 
     /**
diff --git a/src/main/scala/com/databricks/spark/redshift/SchemaParser.scala b/src/main/scala/com/databricks/spark/redshift/SchemaParser.scala
index b69d67d3..d58c6f22 100644
--- a/src/main/scala/com/databricks/spark/redshift/SchemaParser.scala
+++ b/src/main/scala/com/databricks/spark/redshift/SchemaParser.scala
@@ -22,7 +22,11 @@ import org.apache.spark.sql.types._
 
 /**
  * A simple parser for Redshift table schemas.
+ *
+ * Note: the only method which uses this class has been deprecated, so this class should be
+ * removed in `spark-redshift` 0.6. We will not accept patches to extend this parser.
  */
+@deprecated("Do not use SchemaParser directly", "0.5.0")
 private[redshift] object SchemaParser extends JavaTokenParsers {
   // redshift data types: http://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html
   private val SMALLINT: Parser[DataType] = ("smallint" | "int2") ^^^ ShortType
diff --git a/src/main/scala/com/databricks/spark/redshift/package.scala b/src/main/scala/com/databricks/spark/redshift/package.scala
index 129cfb8b..272464be 100644
--- a/src/main/scala/com/databricks/spark/redshift/package.scala
+++ b/src/main/scala/com/databricks/spark/redshift/package.scala
@@ -17,10 +17,9 @@
 
 package com.databricks.spark
 
-import com.databricks.spark.redshift.DefaultJDBCWrapper
-import org.apache.spark.sql.functions._
+import org.apache.spark.sql.functions.col
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.{DataFrame, DataFrameReader, DataFrameWriter, Row, SQLContext}
 
 package object redshift {
 
@@ -47,6 +46,7 @@ package object redshift {
     /**
      * Reads a table unload from Redshift with its schema in format "name0 type0 name1 type1 ...".
      */
+    @deprecated("Use data sources API or perform string -> data type casts yourself", "0.5.0")
     def redshiftFile(path: String, schema: String): DataFrame = {
       val structType = SchemaParser.parseSchema(schema)
       val casts = structType.fields.map { field =>
@@ -59,6 +59,7 @@ package object redshift {
      * Read a Redshift table into a DataFrame, using S3 for data transfer and JDBC
      * to control Redshift and resolve the schema
      */
+    @deprecated("Use sqlContext.read()", "0.5.0")
     def redshiftTable(parameters: Map[String, String]): DataFrame = {
       val params = Parameters.mergeParameters(parameters)
       sqlContext.baseRelationToDataFrame(
@@ -69,11 +70,13 @@ package object redshift {
   /**
    * Add write functionality to DataFrame
    */
+  @deprecated("Use DataFrame.write()", "0.5.0")
   implicit class RedshiftDataFrame(dataFrame: DataFrame) {
 
     /**
      * Load the DataFrame into a Redshift database table
      */
+    @deprecated("Use DataFrame.write()", "0.5.0")
     def saveAsRedshiftTable(parameters: Map[String, String]): Unit = {
       val params = Parameters.mergeParameters(parameters)
       DefaultRedshiftWriter.saveToRedshift(dataFrame.sqlContext, dataFrame, params)