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Organize based on business tasks rather than features of Kite
Organize around the tutorial with JIT information and links to more detail.
Provide incremental versions of tutorial files so that customers can start anywhere
Create this as a portal to get started immediately with Kite, interacting with the CLI, applying that experience to the API.
Tutorials:
1) Installing the Kite CLI
a) If you have the hadoop command (on a
cluster) ? Example required.
b) If you are not on a cluster ? Example
required.
2) Creating a Hadoop dataset using Kite
a) JIT dataset definition with link.
b) JIT schema definition with link
c) Creating a dataset from a CSV file in three
lines.
3) Creating a Hadoop dataset using a schema ?
No support for unstructured HDFS datasets?
a) Working with Kite schemas
i) Building a schema from CSV data
ii) Building a schema using Java objects
b) Creating a Hadoop dataset with a partition
strategy
4) Populating a Hadoop dataset using Kite
a) Transforming with –transform ? Example
required.
b) Loading data with Flume ? Example
required. How to use flume-config
i) Setting up Flume
c) Loading data with Log4J ? Example
required. How to use log4j-config
d) Loading data with Sqoop ? Example
required. What is special about working
with Kite & Sqoop?
i) Transforming input data with an
interceptor – is this a Sqoop feature or
a Kite feature?
Kite data:
1) Introduction to datasets (audience:
anyone new to Kite)
a) What is a dataset? A collection of
records identified by URI and a
description of how to store them
b) Dataset URIs
c) Schemas to describe records Avro schemas
d) Partition strategies to describe
storage layout
2) Views and view URIs
3) Managing datasets over time
a) Schema evolution rules
b) update command
4) HBase datasets (audience: people that
want random-access data)
a) Relevant HBase background (kesy,
data organization)
b) Partition strategies and HBase
c) Column mapping
5) Hive datasets (audience: people with
hive-specific questions)
a) Partitions are created automatically
b) Metadata is stored in the Hive
MetaStore
6) Choosing between Avro and Parquet
7) Partitioning in more depth
8) Column mapping in more depth
Kite data API:
1) Kite and Apache Maven
2) Introduction to datasets in API
a) Dataset methods
i) Creating a dataset
ii) Writing to a dataset
iii) Updating
(1) Data
(2) Schema
iv) Deleting a dataset
b) DatasetDescriptor
i) PartitionStrategy
ii) Avro Schema
c) Avro objects
d) Kite dependencies
3) Views API
a) Selecting records
4) Avro object models
a) Specific
b) Generic
c) Reflect
5) Kite and MapReduce
6) Kite and Crunch
7) Kite and Spark ? What is
unique?
8) HBase dataset API ? Is this
Kite?
a) Key
b) ColumnMapping
c) get/put/delete/increment
Reference:
1) Kite Javadoc
2) Dataset/View URI
reference
3) CLI commands
4) Maven tools
5) Investigating/Interrogating a Hadoop dataset
using Kite
a) Kite and Impala ? What is unique about
Kite when working with Impala?
b) HBase get/put ? Example required. What
is unique about Kite with HBase?
c) Bulk-processing data
i) Using the Kite with the
CrunchDatasets API
ii) Using Kite with the
org.kitesdk.data.mapreduce API
iii) Kite and Spark ? What is unique to
Kite?
iv) Kite and Hive ? What is unique to
Kite?
6) Updating a Hadoop dataset using Kite
a) Adding records to a Hadoop dataset using
Kite
b) Updating a dataset schema using Kite
i) Special considerations when using
Flume ? Example required.