Brian Cotter’s Post

🌟 A Free Solution Providing 13x Reduction in SQL Storage: Data File Size Comparison 🌟 In the realm of data analysis, the format in which data is stored can significantly impact both performance and efficiency. Recently, I conducted an experiment to compare the file sizes of a price transparency dataset from Blue Cross Blue Shield of Massachusetts saved in different formats. Here are the results: .sql file: 5,879,895 KB (including log file) Unzipped .JSON file: 2,680,573 KB Original .JSON.gz file: 487,389 KB .parquet file: 207,564 KB One of the most striking observations is the compact size of the .parquet file compared to the others. This not only reduces storage requirements but also translates to faster data processing. It's also worth noting that the .parquet file was able to load into a Python data frame the fastest, highlighting its efficiency for data manipulation tasks. Remarkably, the .parquet file is approximately 13 times smaller than the unzipped JSON file! This brings us to an important point: the challenges presented by transparency files demand that analysts and organizations think critically about the technology they use to tackle these challenges. By choosing more efficient file formats like Parquet, organizations can reduce the amount of space needed to store files, leading to potential cost savings on storage. For instance, if the storage costs are reduced by 13 times, the overall cost could be significantly lower than current expenses. To illustrate, storing 1 TB of data could decrease from about $276 per year to around $21 per year when using Parquet files. Additionally, faster data processing can reduce the time and resources spent on data analysis, further reducing costs. As data continues to grow in volume and complexity, leveraging appropriate technologies like Parquet can make a significant difference in our ability to manage and analyze data effectively while also being cost-efficient. 🌐💡 #DataAnalysis #BigData #DataScience #FileFormats #Technology #Efficiency #CostSavings #HealthcareData #PriceTransparency

Predicate pushdown is an optimization technique used in databases and query processing. It's designed to improve the efficiency of queries by minimizing the amount of data that needs to be processed. In a database system, when a query is executed, it goes through various stages of processing, such as parsing, optimization, and execution. Predicate pushdown specifically focuses on optimizing the execution phase of a query. Predicates in a query refer to conditions specified in the WHERE clause (or HAVING clause in some cases), which filter the rows based on certain criteria. Predicate pushdown involves the relocation or "pushing down" of these predicates to the lowest possible level within the execution plan of a query. For instance, let's consider a scenario where a query includes multiple operations, such as selection (filtering rows), projection (selecting specific columns), and joins (combining tables). If the query contains predicates that can be applied early in the query execution process (e.g., at the time of accessing the data from storage), this technique allows those predicates to be pushed down closer to the data source before other operations like joins or expensive computations take place. By pushing down these predicates closer to the data source (e.g., database tables), the optimizer can minimize the amount of data fetched or processed, reducing the computational load and improving query performance. This optimization is particularly effective when dealing with large datasets or complex queries involving multiple tables. #dataanalyst #data #dataengineering #apachespark #apache #sql #python #optimization #database

9 Key Points Summarizing Chapter 5 of "Grokking Algorithms" 1. Introduction to hash tables: Hash tables are a data structure that offer fast access to data through key-value pairs. They are widely used due to their average time complexity of O(1) for lookups, insertions, and deletions. 2. Hash function: A hash function is used to convert keys into array indices. A good hash function distributes keys uniformly across the array to minimize collisions and maintain efficiency. 3. Handling collisions: Collisions occur when two keys hash to the same index. Common methods to handle collisions include chaining (storing a list of elements at each index) and open addressing (finding another open slot within the array). 4. Load factor: The load factor is the ratio of the number of elements in the hash table to the total number of slots. Maintaining a low load factor through resizing helps ensure that the hash table remains efficient. 5. Resizing the hash table: When the load factor exceeds a certain threshold, the hash table is resized (typically doubled in size) and all existing keys are rehashed. This helps in maintaining efficient operations. 6. Applications of hash tables: Hash tables are used in various applications such as implementing associative arrays, databases, caching, and sets. They are fundamental to many algorithms and data structures. 7. Advantages of hash tables: The primary advantage of hash tables is their average-case time complexity of O(1) for basic operations. They are also flexible and can handle a dynamic set of keys efficiently. 8. Disadvantages of hash tables: Hash tables can be less efficient if the hash function is poor or if the load factor is too high. Additionally, resizing can be an expensive operation. 9. Example and implementation: Chapter 5 provides a detailed example of implementing a hash table in code, demonstrating how to handle collisions, resizing, and ensuring efficient key-value operations. These points encapsulate the essential concepts and insights from Chapter 5 of "Grokking Algorithms," focusing on the practical implementation and efficiency of hash tables. #HashTables #DataStructures #HashFunction #Collisions #LoadFactor #Resizing #KeyValuePairs #EfficientAccess #ProgrammingConcepts #AlgorithmDesign #ComputerScience #grokking_algorithms

📌 Top 5 advanced SQL commands that are critical and super useful for data engineers: (I learnt it these the hard way 🤓) 𝟏. 𝐖𝐈𝐍𝐃𝐎𝐖 𝐅𝐔𝐍𝐂𝐓𝐈𝐎𝐍𝐒: These are powerful tools for performing calculations across rows related to the current row. Functions like RANK(), DENSE_RANK(), and ROW_NUMBER() are crucial for tasks like ranking and aggregating data within specific partitions. 𝟐. 𝐂𝐓𝐄 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬): CTEs provide a way to break down complex queries into more manageable steps, enhancing code readability. They are essential for recursive queries and creating temporary result sets within a query. 𝟑. 𝐒𝐔𝐁𝐐𝐔𝐄𝐑𝐈𝐄𝐒: Subqueries are nested queries that can be used in various clauses. They're essential for advanced filtering, joining, and aggregating data in a structured manner. 𝟒. 𝐈𝐍𝐃𝐄𝐗𝐈𝐍𝐆: Understanding different indexing techniques (B-tree, Bitmap, Hash) and knowing how to use them efficiently is critical for optimizing query performance, especially when dealing with large datasets. 𝟓. 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: These allow you to encapsulate complex logic into reusable blocks of code. Stored procedures are particularly useful for maintaining and executing complex tasks consistently across different parts of your database. #dataanalysis #dataengineering #datascience #data #datavalidation Women In Data #sql

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗤𝗘 𝗶𝗻 𝗦𝗽𝗮𝗿𝗸? 𝗞𝗲𝘆 𝗣𝗼𝗶𝗻𝘁𝘀 𝘁𝗼 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿 𝗔𝗯𝗼𝘂𝘁 𝗔𝗤𝗘 (𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗤𝘂𝗲𝗿𝘆 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻): - AQE is a feature introduced in Spark 3.0+ that dynamically updates the query plan using metadata information. - It is particularly useful for optimization in scenarios involving data shuffling. 𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗔𝗤𝗘 𝗛𝗲𝗹𝗽? 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝟭: 𝗖𝗼𝗮𝗹𝗲𝘀𝗰𝗶𝗻𝗴 𝗨𝗻𝘂𝘀𝗲𝗱/𝗕𝗹𝗮𝗻𝗸 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝘀 - Imagine you've used a wide transformation, such as `groupBy(dept)`. - If your DataFrame contains only 5 distinct departments, ideally, you should have 5 partitions after shuffling. However, the default setting of `spark.sql.shuffle.partitions` is 200, resulting in 200 partitions. - 𝘼𝙌𝙀 𝙙𝙚𝙩𝙚𝙘𝙩𝙨 𝙩𝙝𝙖𝙩 150 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣𝙨 𝙖𝙧𝙚 𝙚𝙢𝙥𝙩𝙮 𝙖𝙣𝙙 𝙙𝙮𝙣𝙖𝙢𝙞𝙘𝙖𝙡𝙡𝙮 𝙘𝙤𝙖𝙡𝙚𝙨𝙘𝙚𝙨 𝙩𝙝𝙚𝙢, 𝙘𝙤𝙢𝙗𝙞𝙣𝙞𝙣𝙜 𝙩𝙝𝙚 𝙚𝙢𝙥𝙩𝙮 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣𝙨. 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝟮: 𝗥𝗲𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴/𝘀𝗽𝗹𝗶𝘁𝘁𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝘀 - Consider a scenario where one department holds 90% of the total employees after a `groupBy(dept)`, leading to a large partition (which is not ideal). - AQE identifies this imbalance and repartitions the data to manage skewness effectively. - 𝗡𝗼𝘁𝗲: AQE does repartition or splitting of partition only if a certain threshold is crossed. Read the following for more details. ''''' https://lnkd.in/dTed-_FE '''''' 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝟯: 𝗕𝗿𝗼𝗮𝗱𝗰𝗮𝘀𝘁 𝗝𝗼𝗶𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - Suppose you have two DataFrames and, after applying filters, you plan to join them. Initially, you might not know the size of the DataFrame post-filtering. - AQE can assess the size and, if it's smaller than the broadcast threshold you set, it dynamically decides to broadcast the table. 🅸🆂🅽'🆃 🆃🅷🅴 🅰🆀🅴 🅵🅴🅰🆃🆄🆁🅴 🅰🅼🅰🆉🅸🅽🅶? 𝗪𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 𝗲𝗻𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝘂𝘀𝗲 𝗶𝘁? If so, follow me for updates! I’ll be sharing that information soon. 𝗣𝗦: This is not a copy-paste post; I’m sharing my understanding in the simplest words possible, so there might be some mistakes. If you spot any, don’t hesitate to let me know. #learningInPublic is fun, after all. Thank you for reading till the end. See you in the next post! #happyLearning #learning #learningInPublic #spark #AQE #bigdata #dataengineering #distributedComputing

💡 Today we can talk about Data Structure(DS). Data Structure is mainly used to organize data to store or manipulate properly for solving any problems. It is independent of programming Language. It is some predefined way to solve any complexity that arise during creating any software. 📖 DS mainly has 2 types and both of the types has corresponding classifications also. We will discuss about them briefly here: 1. Linear DS Linear DS has 4 types: 1. Array 2. Linked List 3. Stack 4. Queue 2. Non-Linear DS Non-Linear DS has 3 types: 1. Tree 2. Graph 3. Tries 📖 Here we will discuss about only Linear types of DS. So First of all, there is come - 📝 Array: Arrays are the collections of same or different types of data that are stored in the memory sequentially. The the data with in array is called element. And the location of the element within array is called index. The memory address of array and index is not the same thing. 📝 Linked List: Linked list is another types of linear data structure which is the collections of nodes. A node is consists of data value and pointer. Data value contains the value and pointer contains the address of the next node. Linked list has the dynamic size property where the size of array is static. Link list also has some classifications like singly linked list, doubly link list, circular link list. 📝 Stack: Stack is a kind of linear data structure that follows LIFO(Last In Fast Out) principle. It means that the data that will be inserted Last will be deleted first. It allows only one way data operations. If we want to insert any data in the stack we can use push and If we want to get any data from the stack then we have to use pop. 📝 Queue: Queue is another types of linear data structure that follows the FIFO(First In First Out) Principle. It means the data that will be inserted First will be Out from the Queue first. It has 2 end for data operations. One is Frontend and another is Rear end. Front end is used to insert data to the Queue and rear end is used to delete the data from the rear end. The insertion of any data in the Queue is called enqueue and the deletion of any data from the Queue is called dequeue. This is the brief over view of linear data structure. We will know about non linear data structure another day InshaAllah. inspired from #turtorialsPoint DSA

Stack data structure is one of the fundamental data structure that we all should learn. There are many real-life application of Stack data structure. #stack #datastructures #programming #datastructuresandalgorithms https://lnkd.in/gRJFHpD4

Avro, Parquet, ORC file formats: Imagine you have a huge collection of your favorite comic books, and you want to store them in a way that makes it easy to find and read them later.  You have three options: Avro, Parquet, and ORC. Avro is like a comic book in a plastic sleeve with a label on the front. It's simple, lightweight, and easy to understand.  Avro files are compact and can be quickly read, making them great for storing small to medium-sized collections of data that you want to access quickly. Parquet is like a comic book that's been organized into chapters, with an index at the beginning that tells you where to find each chapter.  Parquet files are columnar in nature, meaning they store data in columns instead of rows, which makes them super efficient for analytical queries.  They also support advanced features like compression and encryption, making them a good choice for large collections of data that need to be stored securely and analyzed later. ORC (Optimized Row Columnar) is like a comic book that's been carefully curated and optimized for maximum storage efficiency.  It's like having a comic book librarian who has sorted the pages in the most logical order and removed any unnecessary information.  ORC files are great for storing really large collections of data, and they are designed to be super fast for reading and writing.  They also support advanced features like predicate pushdown and lightweight compression, making them a top choice for big data scenarios where performance is critical. So, in summary, Avro is simple and lightweight, Parquet is organized and efficient, and ORC is optimized and super fast.  Just like how you would choose the best way to store and organize your comic book collection based on your needs, Avro, Parquet, and ORC are different file formats that are designed to suit different types of data storage and analysis requirements. #hive #specializedfileformats #fileformats #data #dataengineering #opentowork #bigdataengineer #bigdata #bigdatadeveloper #bigdataanalysis #bigdatatechnologies #bigdatafresher #bigdataconcepts

The Art of Data Analysis: A Symphony of Tools and Techniques In the realm of data science, the journey of analysis is as diverse as the datasets we encounter. It’s a multifaceted adventure that demands more than just a singular skill or tool—it requires a symphony. The process begins with raw data, often scattered and unstructured, awaiting transformation. Through meticulous cleaning and integration, this data starts to reveal its potential. Tools like BigQuery lay the groundwork for this transformation, allowing for robust storage and sophisticated querying. Yet, the journey doesn't end there. The art of manipulation and query unfolds through languages like Python and SQL, turning raw numbers into refined insights. And in this complex process simplicity finds its place too. The use of CSV files exemplifies this, bridging the gap between intricate data processes and accessible, actionable outcomes. These files, easily opened in tools as simple as the MS Excel, underscore the seamless transition from data to insight. In essence, data analysis is a dynamic field that thrives on the integration of different methodologies and tools. It's a discipline where the combination of the complex and the straightforward paves the way for breakthroughs, emphasizing that versatility isn't just beneficial; it's essential. #DataScience #DataAnalysis #BigData #MachineLearning #Analytics #DataVisualization #PythonProgramming #SQL #CloudComputing #BigQuery