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Four Tableau products a quick review and explanation

I want to share you what are the Products most popular.

Total four products. Read the details below.

Tableau desktop-(Business analytics anyone can use) - Tableau  Desktop  is  based  on  breakthrough technology  from  Stanford  University  that  lets  you drag & drop to analyze data. You can connect to  data in a few clicks, then visualize and create interactive dashboards with a few more.

We’ve done years of research to build a system that supports people’s natural  ability  to  think visually. Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. You just create beautiful, rich data visualizations.  It's so easy to use that any Excel user can learn it. Get more results for less effort. And it’s 10 –100x faster than existing solutions.

Tableau server
Tableau  Server  is  a  business  intelligence  application  that  provides  browser-based  analytics anyone can use. It’s a rapid-fire alternative to th…

The best Spark SQL example showed in 10 steps

Spark SQL example
Spark SQL example
This example demonstrates how to use sqlContext.sql to create and load two tables and select rows from the tables into two DataFrames.

The next steps use the DataFrame API to filter the rows for salaries greater than 150,000 from one of the tables and shows the resulting DataFrame. Then the two DataFrames are joined to create a third DataFrame. Finally the new DataFrame is saved to a Hive table.

1. At the command line, copy the Hue sample_07 and sample_08 CSV files to HDFS:
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_07.csv /user/hdfs
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_08.csv /user/hdfs

where HUE_HOME defaultsto /opt/cloudera/parcels/CDH/lib/hue (parcel installation) or /usr/lib/hue
(package installation).

2. Start spark-shell:
$ spark-shell

3. Create Hive tables sample_07 and sample_08:

scala> sqlContext.sql("CREATE TABLE sample_07 (code string,description string,total_emp
 int,salary int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TextFile")
scala> sqlContext.sql("CREATE TABLE sample_08 (code string,description string,total_emp
 int,salary int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TextFile")

Also Read: Learn SparkSQL by your own with little money

4. In Beeline, show the Hive tables:
[0: jdbc:hive2://hostname.com:> show tables;
+------------+--+
| tab_name |
+------------+--+
16 | Spark Guide
Developing Spark Applications
| sample_07 |
| sample_08 |
+------------+--+

5. Load the data in the CSV files into the tables:
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_07.csv' OVERWRITE INTO TABLE
 sample_07")
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_08.csv' OVERWRITE INTO TABLE
 sample_08")

6. Create DataFrames containing the contents of the sample_07 and sample_08 tables:
scala> val df_07 = sqlContext.sql("SELECT * from sample_07")
scala> val df_08 = sqlContext.sql("SELECT * from sample_08")

Apache Spark
7. Show all rows in df_07 with salary greater than 150,000:
scala> df_07.filter(df_07("salary") > 150000).show()
The output should be:
+-------+--------------------+---------+------+
| code| description|total_emp|salary|
+-------+--------------------+---------+------+
|11-1011| Chief executives| 299160|151370|
|29-1022|Oral and maxillof...| 5040|178440|
|29-1023| Orthodontists| 5350|185340|
|29-1024| Prosthodontists| 380|169360|
|29-1061| Anesthesiologists| 31030|192780|
|29-1062|Family and genera...| 113250|153640|
|29-1063| Internists, general| 46260|167270|
|29-1064|Obstetricians and...| 21340|183600|
|29-1067| Surgeons| 50260|191410|
|29-1069|Physicians and su...| 237400|155150|
+-------+--------------------+---------+------+

8.Create the DataFrame df_09 by joining df_07 and df_08, retaining only the code and description columns.
scala> val df_09 = df_07.join(df_08, df_07("code") ===
df_08("code")).select(df_07.col("code"),df_07.col("description"))
scala> df_09.show()

The new DataFrame looks like:
+-------+--------------------+
| code| description|
+-------+--------------------+
|00-0000| All Occupations|
|11-0000|Management occupa...|
|11-1011| Chief executives|
|11-1021|General and opera...|
|11-1031| Legislators|
|11-2011|Advertising and p...|
|11-2021| Marketing managers|
|11-2022| Sales managers|
|11-2031|Public relations ...|
|11-3011|Administrative se...|
|11-3021|Computer and info...|
|11-3031| Financial managers|
|11-3041|Compensation and ...|
|11-3042|Training and deve...|
|11-3049|Human resources m...|
|11-3051|Industrial produc...|
|11-3061| Purchasing managers|
|11-3071|Transportation, s...|
|11-9011|Farm, ranch, and ...|
+-------+--------------------+

9. Save DataFrame df_09 as the Hive table sample_09:
scala> df_09.write.saveAsTable("sample_09")

10. In Beeline, show the Hive tables:
[0: jdbc:hive2://hostname.com:> show tables;
+------------+--+
| tab_name |
+------------+--+
| sample_07 |
| sample_08 |
| sample_09 |
+------------+--+

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