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SQL Query: 3 Methods for Calculating Cumulative SUM

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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.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; 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

Apache Storm Architecture Tutorial Flowchart

There are two main reasons why Apache Storm is so popular. The number one is it can connect to many sources. The number two is scalable. The other advantage is fault-tolerant. That means, guaranteed data processing.


Apache Storm topologies

The map-reduce jobs process data analytics in Hadoop. The topology in Storm is the real data processor.
The co-ordination between Nimbus and Supervisor carried by Zookeeper

Apache Storm

  1. The jobs in Hadoop are similar to the topology. The jobs run as per the schedule defined.
  2. In Storm, the topology runs forever.
  3. A topology consists of many worker processes spread across many machines. 
  4. A topology is a pre-defined design to get end product using your data.
  5. A topology comprises of 2 parts. These are Spout and bolts.
  6. The Spout is a funnel for topology
Storm Topology

Two nodes in Storm

  1. Master Node: similar to the Hadoop job tracker. It runs on a daemon called Nimbus.
  2. Worker Node: It runs on a daemon called Supervisor. The Supervisor listens to the work assigned to each machine.

Master Node

  • Nimbus is responsible for distributing the code
  • Monitors failures
  • Assign tasks to each machine

Worker Node

  • It listens to the work assigned by Nimbus.
  • It works under the subset of the topology.

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