304 skills found · Page 5 of 11
antoinerrr / Letsencrypt.sh OvhOVH hook script
AdaCore / Git HooksGit hooks Python-based scripts
Tekill / Env DiffEnv-diff is a lightweight library which sync your .env files with .env.dist by composer scripts, hooks or manual running
qubard / ChrolloChrollo is an x86 script hook for Garry's Mod
kevinpiac / HookifyPython script that automatically generates hook functions for given objc Header.h file. Ideal for reverse engineering iOS apps.
kawarimidoll / Deno Dev TemplateMy deno dev template with sample scripts, tests, tasks (including git hooks) and workflows.
marth8880 / Discord Subversion Hook ScriptsSubversion (VisualSVN) hook scripts that post messages to Discord
themonthofjune / IV.1080.ScriptHookModified ScriptHookHook from GTAForums to work without a tertiary dll needed
prog-time / Git HooksUseful shell scripts for git hooks
water5555 / WaterDebugIDA Frida Hook Helper is a plugin for IDA Pro designed to simplify the debugging of .so files and the generation of Frida hook scripts. It supports expression-based jumping (e.g., [[x0] + 0x20]) during debugging and automatically generates Frida hook scripts based on IDA function prototypes, significantly improving reverse engineering efficiency.
dontcallmedom / Github Notify Mlindex.py is a python CGI script that provides a Webhook to be used as a github hook endpoint to send mail to a set of email addresses when specific GitHub events happen
debasishm89 / QHooKqHooK is very simple python script (dependent on pydbg) which hooks user defined Win32 APIs in any process and monitor then while process is running and at last prepare a CSV report with various interesting information which can help reverse engineer to track down / analyse unknown exploit samples / shellcode.
chopmo / Git AutopushVery simple bash script to add "git push" as the post commit hook in a Git repo.
HiveMinds / Tw InstallAutomatically installs Taskwarrior, Taskserver (TODO: and Timewarrior). This project aims to support automated installation of all Taskwarrior hook scripts, configuration flavours etc. with a single command.
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
surge-sh / Example GithooksAn example of using git hooks with npm run scripts and Surge.
intuit / Intuit Spring Cloud Config ValidatorValidation tools for Spring Cloud Config repos: .json, .yam|, .yml and .properties, verified through script or GitHub Pre-receive Hook!
fuhry / Yubikey Hotplugudev hook and accompanying script to lock/unlock a workstation when a yubikey is plugged/unplugged
haseeburrehmanfaheem / Frida Scripts AndroidA collection of Frida scripts for hooking Android APIs, including manager services, file I/O, intents, and onTransact methods. Primarily focused on WeChat exploration but applicable to various Android apps and system services.
azwpayne / JsHookScriptJsHookScript, All the hook scripts I know
