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Day 3/100 🗓️: *continuation of previous post* 🔎Collection Data Types:- ✏ String :- ➡ Strings are declared by using {'single quote'," Double quote", '''Triple quote'''} ➡ Duplicates are allowed. ➡ Ordered data type (we get output in same order which we gave in input) ➡ Homogenous data type. ➡ Supports indexing (positive &negative) ➡ Immutable data type. (we can't reassign the values once we assigned). Example: v='Hello' ✏ List :- ➡ List is declared by using [], empty list can be declared by using `list[]` . ➡ Duplicates are allowed. ➡ Ordered data type . ➡ Homogenous data type. ➡ Supports indexing (positive &negative) ➡ Mutable data type. Example: v=[1,2,3] ✏ Tuple :- ➡ Tuple is declared by using () ,to declare single element comma(,) is mandatory. ➡ Duplicates are allowed. ➡ Ordered data type . ➡ Heterogenous data type. ➡ Supports indexing (positive &negative) ➡ Immutable data type but we can change if any mutable data is available. Example: v=(1,2,3) ✏ Set :- ➡ Set is declared by using { }, empty set can be declared by using `set()` and elements are seperated by using comma (,). ➡ Duplicates are not allowed. ➡ Unordered data type . ➡ Heterogenous data type. ➡ Does not supports indexing. ➡ Mutable data type , we can only add the data but we can't modify. Example: v={1,2,3} , set() ✏ Dictionary :- ➡ Dictionary is declared by using { } , empty dictionary can be declared by using `Dict()` or { } . ➡ Dictionary is the combination of key, value pairs. ➡ Duplicates are not allowed in keys , if keys have duplicates the old value will be replaced with the new values, but values allows duplicates. ➡ Ordered data type until we follow the properties. ➡ Heterogenous data type. ➡ Does not supports indexing. ➡ Keys can only accept immutable or single valued data types but values are immutable. Example: v={'a' :1 ,'b':2} #100daysofcode #python #codechallenge

In data engineering, efficient data management is crucial. SQL Views and Materialized Views are two essential tools that serve different purposes. Here’s a quick look at their importance. 💡 🧩 SQL Views: Dynamic and Flexible SQL Views are virtual tables created from SQL queries. They don’t store data physically but generate it dynamically when queried. Benefits: - Real-Time Data: Always show current data. Perfect for live dashboards. 📊 - Simplified Queries: Encapsulate complex queries for easy access. 🧑💻 - Enhanced Security: Limit data exposure by showing specific data. 🔒 Use Case: Ideal for business intelligence analysts needing real-time data without writing complex SQL every time. 🚀 ⚡ Materialized Views: Speed and Efficiency Materialized Views store query results physically, providing precomputed data for faster access. Benefits: - Improved Performance: Faster querying for complex operations. 🚀 - Reduced Load: Offloads computation from base tables. 📈 - Periodic Refresh: Balance data freshness and performance. ⏱️ Use Case: Great for e-commerce platforms needing quick access to daily sales reports. 🛒 🛠️ Choosing the Right Tool SQL Views: Best for real-time data needs. Materialized Views: Best for performance-critical tasks. Effective use of these tools can optimize data access, improve performance, and enhance scalability. 🌟 #DataEngineering #SQLViews #MaterializedViews #TechTips 🚀

🪄Views in Data Analysis Views and their applications in data analysis. Views in SQL are virtual tables that are derived from the results of a query. They provide a way to simplify complex queries and make them easier to use for end-users. Views can be used to hide sensitive data, aggregate data, or present data in a specific format. Applications of views in data analysis: - Data security: Views can be used to mask sensitive data from users who are not authorized to see it. For example, a view could be created that only shows customer data for a specific region, or for customers who have given their consent to share their data. - Data aggregation: Views can be used to aggregate data from multiple tables. For example, a view could be created that shows the total sales for each product, or the total number of orders for each customer. - Data presentation: Views can be used to present data in a specific format. For example, a view could be created that shows a table of sales data, with the columns sorted by sales amount. Alternatively, a view could be created that shows a bar chart of sales data by product category. Advantages of using views: - Simplicity: Views can make complex queries easier to use for end-users. - Security: Views can be used to hide sensitive data. - Performance: Views can be cached, which can improve performance. - Consistency: Views can help to ensure that data is presented consistently across different reports and dashboards. Disadvantages of using views: - Maintenance: Views can be difficult to maintain, especially if they are based on complex queries. - Performance: Views can sometimes be slower than querying the underlying tables directly. Overall, views are a powerful tool that can be used to improve data analysis in a variety of ways.

Unleashing SQL: Transform Data with the UNPIVOT Function Data normalization and transformation are essential tasks for any data engineer, and sometimes, we need to convert columns into rows to better analyze our data. This is where the UNPIVOT function comes into the spotlight. What is the UNPIVOT Function? The UNPIVOT function does the opposite of PIVOT. It takes column-based data and turns it into row-based data, which can be crucial for normalizing datasets or preparing them for further processing. When to Use UNPIVOT? Suppose you have a dataset where sales figures are split across multiple columns by year (2019 Sales, 2020 Sales, 2021 Sales). You need to convert these into a more traditional row-based format with columns for Year and Sales Amount. The UNPIVOT function can accomplish this easily. Basic Syntax: SELECT Product, Year, SalesAmount FROM SalesData UNPIVOT ( SalesAmount FOR Year IN ([2019 Sales], [2020 Sales], [2021 Sales]) ) AS UnpivotTable; Why Should You Use It? Normalization: Simplifies data structures by turning wide tables into long tables. Flexibility: Makes data more suitable for statistical analysis or integration into other systems. Streamlining: Reduces the complexity of data transformations and prepares your data for advanced queries. The UNPIVOT function is a must-have in your SQL toolkit, especially when dealing with denormalized or wide datasets. By transforming columns into rows, you can make your data more flexible and ready for any analysis or integration task. Have you used the UNPIVOT function in your SQL work? I’d love to hear how it’s helped you simplify your data transformation processes! #DataEngineering #SQL #Unpivot #DataTransformation #DatabaseManagement

What is a Data Analyst? A Data Analyst is someone who transforms data into information that is useful for decision making. Now, to understand this definition we are going to break it down and understand each component of it. First, we ask the question: What is data? and What is information? What is Data? According to Wikipedia, “In common usage, data (/ˈdeɪtə/, also US: /ˈdætə/) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally.” You can find and read the rest of the definition at Wikipedia. I have to say it is a long definition so lets simplify it a little. A Simpler Definition of Data Data are a collection of values that convey information, describing the quantity, quality, fact, statistic, other basic units of meaning. Also, if we said “data are a collection of values” and stopped there, it would still be a good simple definition. Alright, now that we have defined what data is, we want to look at what data looks like. You can find the rest of the post https://lnkd.in/gnJ_haGM

Role of data engineering consultants in improving data efficiency #dataengineeringconsultants #dataengineerconsultants https://lnkd.in/gtP-6-q5 In this article we will learn about the role of data engineer consultants in optimizing business data.

More Than 80% Of The World's Data Is Unstructured. There's A Really Good Chance You Can Drive More Value For Your Business What you see below is a called a "regular expression" (regex) They were first used in software tools in the 1960s. But today might be the first time you have actually ever heard of them. How crazy is that?!? That expression below finds Canadian Postal codes within text. As long as it meets the pattern, it will find it. The pattern for a Canadian Postal Code is A1A 1A1 (the letters and numbers change based on geography and delivery area) Up until recently, these types of tools were used by an organizations data science teams. These days, the opportunity for anyone to use these is everywhere ... even Google Sheets. But why would an operations person ever need to use regex? Here's what inspired this post: I'm doing a major eCommerce analysis for a large multi-national retailer at the moment. If you have been following my posts lately, you may have noticed I'm talked A LOT about the value of merging sales data with a geospatial analysis. I needed to be able to visualize all of the transactions and the density of sales for my client on a heatmap. Since the delivery activity happens through major parcel carriers, the key element needed as to pin the sales activity to either the Zipcode or the Canadian FSA. Do you know what 99% of retailers don't do? Geocode their order data by recipient address. Luckily, I already have a full list of geocodes for zips and FSAs. So it was a straightforward match. Except the data was dirty. Analysts estimate that 1/3 of business data is dirty and not suitable for analytics. It's costing business up to 12% of their bottom line when you attribute time spend cleaning it, or lost sales insights from not being able to access it. When you have a few million rows of transaction data, Excel isn't going to cut it. I needed to filter out transactions that had bad address data, and extract the postal code data when the field was incorrectly formatted (a bad system integration dropped more than it should have into the field). Solution: the regex below The output? A massive list of data transactions that were immediately matched to their geographic region. I can work with and fix these types of issues, it's an example of why I am supporting clients with their master data, data capture quality, hierarchies and customer dimensions. Because it dramatically levels up business decisions (and usually helps find a lot of ways to be more profitable). Want to improve your data quality and help deliver better business insights? -------- ---------- ---------- ---------- ---------- Would your network find this interesting? Be the first to get it to their feed by commenting or sharing Interested in Deeper Dives right to your inbox 1x/wk 👇 Take 10 seconds to paste your email here: https://lnkd.in/dCsUAauR Want to submit a question for me to write about 👇 AMA: https://lnkd.in/e3RjBiJD

Simplified Data Analytics for the Non-Technical Professional Data analytics has become an integral part of decision-making in businesses across various industries. However, the complexity of data analysis tools and techniques can be daunting for non-engineers. This article demystifies data analytics and offers insights into user-friendly tools and methods that empower professionals with little to no engineering background to harness the power of data. Read more

In my latest SQL project, I explored advanced data analysis techniques, Using subqueries to answer complex business questions about sales performance. Subqueries, or nested queries, were the week's focus as they enabled a deeper dive into data, revealing insights that simple queries couldn’t capture. Here’s a glimpse into what I achieved: 1- Identifying Top Sales Days: I began by determining the dates with the highest total sales, then identified which products drove these peaks. This analysis allowed us to pinpoint key dates and assess product performance. Finding Premium Products by Price: I calculated each product’s average unit price and highlighted those with the highest averages. This helped to spotlight premium items that could benefit from targeted marketing or special promotions. 2- The Product Performance on High-Quantity Days: Using subqueries, I analyzed dates where product quantities sold exceeded the average, providing valuable insight into demand surges. 3- Top Dates by Total Sales: To understand sales patterns, I used subqueries to isolate the top three days with the highest sales, along with the specific products contributing to each day’s performance. 4- Contribution Percentages on Key Dates: I calculated each product’s percentage of total sales on April 15th, 2024, to understand how individual products drove overall sales, illustrating valuable trends in customer preferences. 5- Peak Sales Over 3-Day Periods: I utilized subqueries to find the maximum total sales for each product over any three consecutive days, helping to spot periods of exceptional performance. 6 - Comparing Product C’s Sales: I also identified dates where Product C’s sales exceeded the combined sales of all other products, offering a unique view of its performance. This project provided hands-on experience, leveraging SQL subqueries for complex, layered data analysis. Each query is built on the last, allowing me to extract meaningful insights and gain a strategic view of the sales landscape. I am Looking forward to applying these SQL techniques to drive data-informed decisions in the near future when dealing with real live data. TheData Immersed(TDI) Anne Nnamani

Delighted to share my blog where I have explained how different Data Professionals contribute in a single project and the essential skill sets required for becoming any Data Professional. Please visit my blog for more insights- https://lnkd.in/eYaMjQ_h #dataprofessionals #skills