Showing posts with the label Cloudera Impala

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SQL Query: 3 Methods for Calculating Cumulative SUM

SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON >= GROUP BY, t1.value ORDER BY; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

Cloudera Impala top features useful for developers

Cloudera Impala that runs on Apache Hadoop. The program was proclaimed in October 2012 with a common beta trial dispersion. Popular usage is in data analytics.The key features useful for interviews. Impala The Apache-licensed Impala program begets scalable collateral database techniques to Hadoop, authorizing consumers to subject low-latency SQL requests to information kept in HDFS and Apache HBase short of needing information motion either alteration. Impala is amalgamated with Hadoop to employ the similar file and information setups, metadata, safeguarding and asset administration architectures applied by MapReduce, Apache Hive, Apache Pig and different Hadoop code. Impala Applications Impala is advanced for experts and information experts in science to accomplish systematic computational analysis of data or statistics on information kept in Hadoop through SQL either trade intellect implements.    The effect is that extensive information handling (via MapReduce) and two-way req