Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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Beginner's Tutorial on SaS Visual Analytics
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SAS visual analytics is a completely new architecture from SAS. It has the capability to manage large amounts of data and bring it into memory to analyze it, explore it and publish reports.
Although the data amounts are massive — up to 1.1 billion rows of data, the SAS LASR Analytic Server, to use its full name, was designed to be intuitive to users without an advanced degree in computer science.
A report from Simply hired.
All about SAS analytics Server - The SAS Analytic Server begins with an eight-blade server with 96 processor cores, 768 gigabytes memory and 4.8 terabytes (TB) of disk storage.
The upper end of the reference configurations is 96 blades with 1,152 cores, 9.2 TB memory and 57.6 TB of disk storage, enough disk space to store the entire Library of Congress six times.
Where to Learn SAS Visual Analytics
The speed of in-memory architecture offers tremendous benefit. Organisations can explore huge data volumes and get answers to critical questions in near-real time. SAS Visual Analytics offers a double bonus: the speed of in-memory analytics plus self-service eliminates the traditional wait for IT-generated reports.
Businesses today must base decisions on insight gleaned from data, and that process needs to be close to instantaneous.
Despite being user-friendly, the server has been developed to make it easy for IT to manage the data and secure it without sacrificing usability, Guard said. It includes a visual analytics explorer for ad hoc analysis and discovery, he added.
SAS Visual Analytics helps business users to visually explore data on their own. But it goes well beyond traditional query and reporting.
Running on low-cost, industry-standard blade servers, its high-performance in-memory architecture delivers answers in seconds or minutes instead of hours or days.
Where SAS differs
SAS analytics differ from many business intelligence (BI) solutions which simply move data from a SQL database into memory. That does not support regressions or logistics models becase those capabilities are not built into databases.
In banking, analysts may develop hundreds of models a year; with SAS they will be able to do it 10 to 20 times faster. The importance of changing models rapidly is incredibly important in the banking industry.
A demo on SAS visual analytics:
The computerWorld says-SAS also plans to broaden its user base by making its software more appealing beyond computer statisticians and data scientists.
To this end, the company has paired its data exploration software, called SAS Visual Analytics, with its software for developing predictive models, called SAS Visual Statistics.
The pairing can allow non-data scientists, such as line of business analysts and risk managers, to predict future trends based on current data.
How companies will benefit
With SAS Analytic Server companies can solve problems they had never dealt with before because they it offers speed of analysis at a large scale. Users don’t have to analyze samples; they can look at everything.
AS Visual Analytics will let us quickly dig into our big data to uncover opportunities, and in time, to fully exploit them.”The SAS LASR Analytic Server, uses Hadoop (embedded Hadoop Distributed File System) as local storage at the server for fault tolerance.
SAS LASR Analytic Server has been tested on billions of rows of data and is extremely scalable, bypassing the known column limitations of many relational database management systems (RDBMS).
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime are the same. I'll show you how this error happens and its resolution. Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error
Here's the command to check the list of brokers present in a Kafka Cluster. You can say the Broker is the heart of the Kafka cluster. In simple terms, it works as a process. The main function is to receive messages from publishers and gives permission to access messages by consumers. How to Check Available Brokers in Kafka Here is the command: linux$ ./zookeeper-shell.sh zookeeper-IPaddress:2181 | "ls /brokers/ids" Just use the above command to get the number of brokers present in your host (Kafka Cluster). What is Default Broker-id in Kafka The default broker id is -1 . When you create a new broker, it adds to -1. Then, it gives a new broker id. The broker ids will be generated from reserved.broker.max.id + 1. Types of broker ids. Use assigned Zookeeper generated broker id. According to Wiki: Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes. Additionally, partitions are replicat
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
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