Cloudera Impala and Apache Hive provide a better way to manage structured and semi-structured data on Hadoop ecosystem. Both frameworks make use of HDFS as a storage mechanism to store data. The HDFS architecture is not intended to update files, it is designed for batch processing. i.e. process huge amount of data. But most of the organizations are maintaining a data warehouse on traditional relation databases like Netezza, Teradata, Oracle, etc. When they migrate their data warehouse to Hadoop ecosystem, they might want to have a design similar to that…

Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored on bigdata Hadoop. Cloudera Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase. In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL), ODBC driver, and user interface as Apache Hive. In this article, we will check different methods to access Impala tables from python program or script. The methods we are going to discuss here will help you to connect Impala…

Data warehouse stores the information in the form of tables. You may have to delete out-dated data and update the table’s values in order to keep data up-to-date. These performance critical operations are critical to keep the data warehouse on bigdata also when you migrate data from relational database systems.  In this article, we will check Impala delete from tables and alternative examples. Impala Delete from Table Command Cloudera Impala version 5.10 and above supports DELETE FROM table command on kudu storage. This command deletes an arbitrary number of rows…

The SQL set operators are used to combine data from two or more SELECT statements. The set operators can combine only similar data sets. Here similar data set literally means the number of columns and its data type should match, otherwise you must explicitly type cast the types of the values or columns in the SELECT statements. Just like Apache hive, Impala support only UNION and UNION ALL set operator, INTERSECT and MINUS are not supported as of now. In this article, we will check SQL set operator MINUS alternative…

A View creates a pseudo-table or virtual table. It appears exactly as a regular table, you can use it in SELECT statements, JOINs etc. The Impala CREATE VIEW statement allows you to create a shorthand abbreviation for a more complicated query. The base query can have tables, joins, column alias etc. In this article, we will check Cloudera Impala create view syntax and some examples. Just like views or table in other database, an Impala view contains rows and columns. The fields in a view are fields from one or…

In this article, we will discuss on the various Cloudera Impala string functions and usage. The Impala SQL string functions are similar to the SQL string functions. Cloudera Impala String Functions The commonly used string functions in Cloudera Impala are listed below: Impala String Functions Descriptions ascii(string str) Returns the numeric ASCII code of the first character of the argument. btrim(string a) btrim(string a, string chars_to_trim) Removes all instances of one or more characters from the start and end of a STRING value. Optionally, you can provide characters to be…

The Cloudera Impala regular expression functions identify precise patterns of characters in the given string and are useful for extracting string from the data and validation of the existing data, for example, validate date, range checks, checks for characters, and extract specific characters from the data. In this article, we will be checking some commonly used Cloudera Impala regular expression functions with an examples. Types of Cloudera Impala Regular Expression Functions As of now, Cloudera Impala supports only three regular expression functions: regexp_extract regexp_like regexp_replace Impala regexp_extract Function The Impala…