The sql middle finger Hoodie Trust Me, I'm A Database Administrator Hoodie "I Run The Database" Hoodie Select * from users - Hoodie "I store my data in MySQL" Hoodie "I Have Databases" Hoodie SQL: Structured Query Language Hoodie I <3 Replication Hoodie Cute Sql server Hoodie "DB Evil Genius" Hoodie SQL DBA Developer Individuality Hoodie Database Diva Fitted Hoodie "I Speak T-SQL" Hoodie World's Coolest CLINICAL DATA MANAGER Hoodie 3NF: close enough for rock & roll Hoodie "I Speak SQL" Hoodie "I Love a DB Pro" Hoodie I Speak SQL Mug Mugs select * from users where clue > 0 The sql middle finger Mug TSQL JOIN TYPES Mug Cute Sql Travel Mug No Comment 1 Shirt No Comment 1 T-Shirt When I grow up I want to be a Database Administrat SQL Pimp - T-Shirt DBAs Do It On Tables Mug Its not a bug! Jointures SqlEn SqlJoins InfographicLes JointuresCheat Sheet ProgrammingSql Cheat SheetSql ProgrammingProgramming PythonProgramming LanguagesForwardInfographic of the 7 kind of SQL Joins.

Including : INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, with or without the intersect. Very useful for web developer. Source : http://sql.sh #SQL #JOINThe requested URL /home.php?cat=108 was not found on this server.Hoodie manages storage of large analytical datasets on HDFS and serve them out via two types of tables Read Optimized Table - Provides excellent query performance via purely columnar storage (e.g. Parquet) Near-Real time Table - Provides queries on real-time data, using a combination of columnar & row based storage (e.g Parquet + Avro)ulu hoodie By carefully managing how data is laid out on storage & how its exposed to queries, Hoodie is able to power a rich data ecosystem where external sources can be ingested into Hadoop in near-real time.roh hoodie The ingested data is then available for interactive SQL Engines like Presto & Spark,goku hoodie amazon

while at the same time capable of being consumed incrementally from processing/ETL frameoworks like Hive & Spark to build derived (hoodie) datasets. Hoodie broadly consists of a self contained Spark library to build datasets and integrations with existing query engines for data access. Hoodie is a young project. Near-Real time Table implementation is currently underway. We cannot locate the page you are looking for. You may have followed an outdated link or perhaps you typed in an invalid URL (web address).nebbia hoodie One of the following steps may help you find what you're looking for. Click this link or hit the back button on your browser to return to the previous page. Try each level in clickable form: Correct the URL you have requested, either on your browser or in the field below:School: Hi, this is your son's school. We're having some computer trouble. Mom: Oh, dear -- Did he break something?

School: In a way. Did you really name your son Robert'); Little Bobby Tables we call him. School: Well, we've lost this year's student records. I hope you're happy. Mom: And I hope you've learned to sanitize your database inputs. See the sidebar to the left for your specific language. Don't see a programming language that you'd like to see represented? Please let me know if you have updates or additions through one of these methods, in decreasing order of preference. I'm looking for people to host non-English versions of this site. Much of the translation work for German, Russian and Spanish hasIf you're interested, please sign up for the bobby-tables mailing list on Google Groups and let us know. Thanks to the following folks for their contributions:It has taken a month to fuss over my new blog, but I finally made my first SQL entry. Since I’m excited about the upcoming SQL Pass conference, I thought I would show a fictitious problem about employees and their interests in SQL Conferences.

Problem: You are given two tables. The first table contains employees. The second table contains all the SQL Conferences each employee has been interested in along with the date they showed interest in the conference and whether or not they are still interested. You are asked to find the last SQL Conference that was added for each employee. Only conferences the employees are still interested in should be included, and only one conference per employee should be listed. The returned data should be ordered by the employee’s last name and first name. The first solution that came to mind, was to use the MAX function on the InterestAddDate field to find the last added interest. There are two issues with this approach though. 1. In order to get the activity field returned, the Interest table has to be joined a second time on the MAX(InerestAddDate). 2. Multiple rows will be returned if the employee had an interest in two SQL conferences on the same date. While this could be a valid result set, in this case only one activity should be returned.

e.FirstName + + e.LastName  EmployeeName Solution: To address these two issues, I used a Common Table Express (CTE)  and the ROW_NUMBER function. This function will number each row with a unique sequential number based on the OVER clause. Inside the OVER clause, I will order the data by the InterestAddDate field in descending order. Since I want to find the last SQL Conference of interest for each employee, I’m going to add the PARTITION statement on the EmployeeID field to the OVER clause. This will cause the ROW_NUMBER function to start over for each EmployeeID. Since I’m not using an aggregate function, I can return all the data from the Interest table that I need. In the next part of the query , I join the CTE to the Employee table and add a WHERE clause. Since I ordered each partition in descending order, I know that the first row of each partition will have a rowindex of 1. I can now filter my data by rowindex = 1. ,() ( i.EmployeeID i.InterestAddDate )  RowIndex

e.FirstName + + e.LastName AS EmployeeNameEmployee e i.EmployeeID = e.EmployeeID When I looked at the logical reads for these two separate queries, the query using the MAX function had twice as many logical reads as the query with the ROW_NUMBER function. When I looked at the Execution Plan for both queries, I found the query using the MAX function had a higher Query Cost relative to the batch. My first run with the data, I used 20 Employees and 40 Interests. For the second run, I used 1000 employees and 4000 interests. I found that the Query Cost for the query using the MAX function increased with the larger datasets. Category: SQL Functions | Tags: Functions, MAX, OVER, PARTITION, ROW_NUMER, SQL I’m very excited about attending my first SQL Pass Summit in Seattle in a few weeks. I haven’t even attended yet and I’m so impressed with the schedule, extracurricular events, and the mentors they assign first-timers like myself. One of the events they have schedule is on Wednesday.

They want everyone to wear their SQL Saturday t-shirts. There is only one problem…..mine makes me look like a house. I absolutely refuse to where a men’s t-shirt that makes me look like that. So I tapped into the right side of my brain and pimped my t-shirt out. Now not only do I look like a girl, but a stylish one. Not looking too great. Look out how happy I look. Now I look like a SQL girl. Growing up my grandmothers taught me crocheting and sewing. (My maternal grandmother taught me in Spanish. ) Now a days, I just go to Google or You-Tube and search for the technique I want to learn. I found out how to alter a neckline and how to add a hoodie. Here are the steps I went through.I researched all the techniques I needed to learn in order to feel comfortable making the changes to my precious SQL Saturday t-shirt. I answered questions like… What kind of needle should I use? Do I need special thread? Do I need any special notions like binding? (Notions are all the little extras you use for sewing, like grommets for the hoodie, clasps, binding,…

The stores have whole walls for notions.) How do you sew a hoodie? Here are the sites I found most helpful based on the sewing skills I already posses. If you have never sewn, I would suggest looking for some YouTube videos on the subject as well. How-to Make a Hoodie, Hollywood Renegade, ThreadbangerI figured out what aspects of my SQL Saturday t-shirt I wanted to alter Neckline – I prefer a scoop neck or v neck. (Next time I will make a v neck.) Shoulders – The shoulder seams needed to be moved up to my shoulders. I think this, out of all the issues with my t-shirt, made me look the boxiest. By moving the seams up to the edge of my shoulder where they belonged, I improved the look of my t-shirt immensely. Sleeves – I’m only 5’6, so my sleeves needed to be shorter. Since I had an extra t-shirt that was in a contrasting color, I used it to change the color of my sleeves. Length – I shortened the t-shirt so that it sat on my hips properly

Hoodie – Because I just needed it.I marked up my t-shirt so that I knew where I needed to cut it. While the video tutorials talked about using scissors to cut the t-shirt up, I found using my rotary cutter much easier. I folded my t-shirt in half, making sure there were no wrinkles, then I cut the sides, arms, and neckline out. Since I folded the t-shirt in half, I knew my t-shirt would remain symmetric. Clocks and plates make great templates for cutting circles.I created a pattern for the hoodie based on my favorite hoodie in my closet. I did buy some red t-shirt material for the hoodie, but I could have used another t-shirt. Since Red Gate is my absolute favorite toolset, I cut their logo out of a second t-shirt I had and put it in my hood. I used that same t-shirt for my sleeves and the rest of the hoodie.I sewed the hoodie together.I sewed the sides and the sleeves.I attached the hoodie to the neckline. I did this by finding the center line of the back of the t-shirt, and the center line of the hoodie.