138 skills found · Page 3 of 5
tdmitch / SSISCatalogDashboardA simple set of reports showing execution details in the SQL Server Integration Services (SSIS) catalog
hudacbr / Webpwn3rwebpwn3r ======== WebPwn3r - Web Applications Security Scanner. By Ebrahim Hegazy - @Zigoo0 Thanks: @lnxg33k, @dia2diab @Aelhemily, @okamalo Please send all your feedback and suggestions to: zigoo.blog['at']@gmail.com How to use: 1- python scan.py 2- The tool will ask you if you want to scan URL or List of urls? 1- Enter number 1 to scan a URL 2- Enter number 2 to scan list of URL's 3- URL should be a full link with a parameters .e.g http://localhost/rand/news.php?com=val&id=11&page=24&text=zigoo same thing with the list of links. Demo Video: https://www.youtube.com/watch?v=B6kDUk-ehOE In it’s Current Public [Demo] version, WebPwn3r got below Features: 1- Scan a URL or List of URL’s 2- Detect and Exploit Remote Code Injection Vulnerabilities. 3- ~ ~ ~ Remote Command Execution Vulnerabilities. 4- ~ ~ ~ SQL Injection Vulnerabilities. 5- ~ ~ ~ Typical XSS Vulnerabilities. 6- Detect WebKnight WAF. 7- Improved Payloads to bypass Security Filters/WAF’s. 8- Finger-Print the backend Technologies. More details: http://www.sec-down.com/wordpress/?p=373
l-v-yonsama / Db NotebookJavascript, SQL creation and execution, Markdown, etc. can be centrally managed in a file format called a notebook.
horizon3ai / CVE 2024 29824Ivanti EPM SQL Injection Remote Code Execution Vulnerability
rusanu / Async TsqlAsynchronous T-SQL procedure execution
method5 / Method5Remote execution for Oracle SQL, PL/SQL, and shell scripts, built entirely inside Oracle. Method5 lets you easily run commands quickly and securely on hundreds of databases.
quartzdesk / Quartzdesk ExecutorQuartzDesk Executor (QE) is a scalable and generic job scheduling application that can be used to schedule execution of native shell scripts (*.sh, *.bat, *.cmd, ...), executable files (*.exe, ...), SQL commands, HTTP POST requests etc. QE can be, for example, used as a replacement of traditional Unix/Linux Cron-based scheduling systems.
daniel3303 / AgentQLReusable .NET library that translates EF Core models into LLM-friendly schema descriptions and provides safe SQL query execution for AI agents.
tanimutomo / SqlfileA Golang library for executing SQL file easily. (support multiple queries execution)
himselfv / Jet ToolMS Jet database schema export/SQL execution tool
iricartb / Advanced Sql Injection ScannerIvan Ricart Borges - Test for didactic purposes of web pages vulnerables to SQL injection using dbo database user with xp_cmdshell execution permissions. Using patterns from Internet search engines to extract potentially vulnerable web addresses and test them by changing the GET parameters using invalid Transact-SQL conversion function to cause through unhandled errors by IIS web server to show critical information. If certain features are given and using advanced injection techniques a malicious attacker could gain control of the entire system by executing shell commands in the SQL database engine.
azutoolkit / CqlCQL Toolkit is a comprehensive library designed to simplify and enhance the management and execution of SQL queries in Crystal. This toolkit provides utilities for building, validating, and executing SQL statements with ease, ensuring better performance and code maintainability.
ultranet1 / APACHE AIRFLOW DATA PIPELINESProject Description: A music streaming company wants to introduce more automation and monitoring to their data warehouse ETL pipelines and they have come to the conclusion that the best tool to achieve this is Apache Airflow. As their Data Engineer, I was tasked to create a reusable production-grade data pipeline that incorporates data quality checks and allows for easy backfills. Several analysts and Data Scientists rely on the output generated by this pipeline and it is expected that the pipeline runs daily on a schedule by pulling new data from the source and store the results to the destination. Data Description: The source data resides in S3 and needs to be processed in a data warehouse in Amazon Redshift. The source datasets consist of JSON logs that tell about user activity in the application and JSON metadata about the songs the users listen to. Data Pipeline design: At a high-level the pipeline does the following tasks. Extract data from multiple S3 locations. Load the data into Redshift cluster. Transform the data into a star schema. Perform data validation and data quality checks. Calculate the most played songs for the specified time interval. Load the result back into S3. dag Structure of the Airflow DAG Design Goals: Based on the requirements of our data consumers, our pipeline is required to adhere to the following guidelines: The DAG should not have any dependencies on past runs. On failure, the task is retried for 3 times. Retries happen every 5 minutes. Catchup is turned off. Do not email on retry. Pipeline Implementation: Apache Airflow is a Python framework for programmatically creating workflows in DAGs, e.g. ETL processes, generating reports, and retraining models on a daily basis. The Airflow UI automatically parses our DAG and creates a natural representation for the movement and transformation of data. A DAG simply is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG describes how you want to carry out your workflow, and Operators determine what actually gets done. By default, airflow comes with some simple built-in operators like PythonOperator, BashOperator, DummyOperator etc., however, airflow lets you extend the features of a BaseOperator and create custom operators. For this project, I developed several custom operators. operators The description of each of these operators follows: StageToRedshiftOperator: Stages data to a specific redshift cluster from a specified S3 location. Operator uses templated fields to handle partitioned S3 locations. LoadFactOperator: Loads data to the given fact table by running the provided sql statement. Supports delete-insert and append style loads. LoadDimensionOperator: Loads data to the given dimension table by running the provided sql statement. Supports delete-insert and append style loads. SubDagOperator: Two or more operators can be grouped into one task using the SubDagOperator. Here, I am grouping the tasks of checking if the given table has rows and then run a series of data quality sql commands. HasRowsOperator: Data quality check to ensure that the specified table has rows. DataQualityOperator: Performs data quality checks by running sql statements to validate the data. SongPopularityOperator: Calculates the top ten most popular songs for a given interval. The interval is dictated by the DAG schedule. UnloadToS3Operator: Stores the analysis result back to the given S3 location. Code for each of these operators is located in the plugins/operators directory. Pipeline Schedule and Data Partitioning: The events data residing on S3 is partitioned by year (2018) and month (11). Our task is to incrementally load the event json files, and run it through the entire pipeline to calculate song popularity and store the result back into S3. In this manner, we can obtain the top songs per day in an automated fashion using the pipeline. Please note, this is a trivial analyis, but you can imagine other complex queries that follow similar structure. S3 Input events data: s3://<bucket>/log_data/2018/11/ 2018-11-01-events.json 2018-11-02-events.json 2018-11-03-events.json .. 2018-11-28-events.json 2018-11-29-events.json 2018-11-30-events.json S3 Output song popularity data: s3://skuchkula-topsongs/ songpopularity_2018-11-01 songpopularity_2018-11-02 songpopularity_2018-11-03 ... songpopularity_2018-11-28 songpopularity_2018-11-29 songpopularity_2018-11-30 The DAG can be configured by giving it some default_args which specify the start_date, end_date and other design choices which I have mentioned above. default_args = { 'owner': 'shravan', 'start_date': datetime(2018, 11, 1), 'end_date': datetime(2018, 11, 30), 'depends_on_past': False, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=5), 'catchup_by_default': False, 'provide_context': True, } How to run this project? Step 1: Create AWS Redshift Cluster using either the console or through the notebook provided in create-redshift-cluster Run the notebook to create AWS Redshift Cluster. Make a note of: DWN_ENDPOINT :: dwhcluster.c4m4dhrmsdov.us-west-2.redshift.amazonaws.com DWH_ROLE_ARN :: arn:aws:iam::506140549518:role/dwhRole Step 2: Start Apache Airflow Run docker-compose up from the directory containing docker-compose.yml. Ensure that you have mapped the volume to point to the location where you have your DAGs. NOTE: You can find details of how to manage Apache Airflow on mac here: https://gist.github.com/shravan-kuchkula/a3f357ff34cf5e3b862f3132fb599cf3 start_airflow Step 3: Configure Apache Airflow Hooks On the left is the S3 connection. The Login and password are the IAM user's access key and secret key that you created. Basically, by using these credentials, we are able to read data from S3. On the right is the redshift connection. These values can be easily gathered from your Redshift cluster connections Step 4: Execute the create-tables-dag This dag will create the staging, fact and dimension tables. The reason we need to trigger this manually is because, we want to keep this out of main dag. Normally, creation of tables can be handled by just triggering a script. But for the sake of illustration, I created a DAG for this and had Airflow trigger the DAG. You can turn off the DAG once it is completed. After running this DAG, you should see all the tables created in the AWS Redshift. Step 5: Turn on the load_and_transform_data_in_redshift dag As the execution start date is 2018-11-1 with a schedule interval @daily and the execution end date is 2018-11-30, Airflow will automatically trigger and schedule the dag runs once per day for 30 times. Shown below are the 30 DAG runs ranging from start_date till end_date, that are trigged by airflow once per day. schedule
rusanu / Com.rusanu.dbutilSQLCMD mode SQL script execution library
subhamX / Train Ticketing🚄 A powerful Railway Ticket Booking Portal built using React, Node, PostgreSQL. Using dynamic SQL techniques, stored procedures, triggers for consistency, and faster query execution. [Part of CS301 course]
umbraco / Join Monster DotnetA .net implementation of Join Monster - A GraphQL to SQL query execution layer for query planning and batch data fetching
AnilSener / Axa Insurance Telematics KaggleI developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two versions of my code (test and full execution). Since AWS costs have exceeded my budget I sopped to train my model(s) all dataset for full dataset execution. There is also a ppt that presents my outputs in test execution. Full Data Execution code is more production ready and slightly different version. I had to use Databricks Table Caching to TRAIN and TEST data tables to obtain acceptable performance in production ready version.
florianjuengermann / Query GodQueryGod lets you interact with any API or database using natural language. Writing simple prompts you can chain together the execution of any SQL query and API call.
devspexx / CentralDatabaseA lightweight asynchronous SQL execution library for Minecraft plugins built on HikariCP with type-safe query results.
erfjab / SqlarSqlar is a Telegram bot designed for simple and fast SQL command execution directly from your Telegram interface.
