Snowflake cloud database with ELT(Airflow+Python+Talend)

Set up the Snowflake environment by pasting a 73-statement DDL file to create the demo db, tables, stages, and functions, preparing the course environment for ELT workflows.

Download and install VirtualBox, import the provided virtual machine appliance, name VM, and ensure a base folder with about 45 GB of space, with BIOS virtualization enabled for the import.

Mount a local folder to a virtual machine for an Airflow ELT workflow, map the dags folder, start the VM, and verify real-time syncing of local documents.

Learn to install and configure Visual Studio Code, install the remote development extension, and connect to the Airflow server to access and run code from the mounted Airflow folder.

Configure Snowflake connection details across Airflow, Talend, and Python projects by updating account, username, password, and region in multiple config files to enable ELT workflows.

Airflow introduces a batch oriented orchestration framework using Python to build scheduled data pipelines as directed acyclic graphs with rich scheduling and an open source web interface for monitoring.

Explore Airflow architecture coordinating python-based data workflows stored in a bag folder, scheduled by the flow scheduler, executed by flow workers, and monitored via metadata db and web server.

Create your first Airflow dag in Python, configure default args and retries, and orchestrate tasks with dummy, Python, and bash operators while clarifying dag versus task.

Connect to the Airflow metadata database, inspect the public schema to view dags and tables, and understand how the flow scheduler stores workflow actions and audit information.

Start the flow server and scheduler in Airflow, connect to the metadata database, and verify the first job entry. Learn how tasks are extracted and scheduled based on dependencies.

Explore how start date and schedule interval drive Airflow DAG execution, including first run timing, daily intervals, and what null intervals and end dates mean for stopping or delaying runs.

Start the Airflow web server and scheduler, view your job in the web console, and monitor task runs, backfilling past days, with results stored in the metadata database.

Learn to diagnose a failed Airflow task by inspecting logs, fix the issue, and retrigger or clear the task to reschedule downstream tasks.

Learn how to load data from Airflow to Snowflake using Airflow, and how to copy and process data with Snowflake Operator in upcoming lectures.

Offload data from Airflow to Snowflake by using a Snowflake operator in a DAG to create, load, and drop a table with templated names.

Create a Snowflake connection in Airflow, run a DAG to create a test table, insert data, and drop it, then review the Snowflake operator output and history.

Demonstrates using a Snowflake hook in an Airflow ELT workflow to create a table, insert data, and fetch a raw count, printing results to logs and guiding task flow decisions.

Copy data from a three location stage to a Snowflake table using the three to Snowflake operator, then process and load it back, filtering by city.

Explore the Snowflake Python API fundamentals, including connection and cursor objects, methods like execute and fetch, attributes, exception handling, bindings, and integration with Pandas and Talend.

Connect to Snowflake using the Snowflake Python connector, create a connection and cursor, run select current_version(), and note that credentials will be stored separately in a future lecture.

Learn how to securely manage Snowflake credentials by storing them in a separate credentials json file, reading them with a helper, and establishing a connection to obtain a cursor.

Explore how the Snowflake Python cursor execute method runs commands, handles optional parameters like time and file stream, and supports uploading data with put and copy commands, including timeouts.

Explore cursor attributes in Snowflake, including the query and the description of the statement, the current connection, and how to use the describe option to fetch metadata without executing.

Explore how to use execute many for batch inserts in Snowflake, binding parameters and lists of values to a single statement, enabling efficient insertion of multiple rows.

Learn to execute asynchronous queries in Snowflake, monitor progress with is still running and get query status, and fetch results using fetch all, fetch one, and get results from cursor.

Learn to execute asynchronous statements using a context object's cursor and connection, monitor query status with get query status through if error, and handle results across new connections.

Learn to use the connection object's is in error and get query status methods to manage errors and update the audit table during Snowflake queries.

Explore pyformat in Snowflake: use q mark binding, numeric binding, and Python format bindings to parameterize queries, insert data, and run in clause and select statements.

Explore numeric and q-mark bindings in Snowflake, compare BI format to q-mark binding, and learn to insert data using parameters with proper date-time mapping and prepared statements.

The lecture demonstrates how the cursor's describe method returns a result metadata object, detailing column attributes, type codes, and internal sizes, and shows how to access them via indexing.

Explore how a result metadata object uses a type code to indicate a column's data type. It discusses values 0 and 2 and their link to the description method.

Compare fetching data from Snowflake using pandas versus Arrow, highlighting Arrow's faster performance and quicker filtering for large datasets.

Explore how a context manager controls Snowflake transactions by wrapping multiple statements, automatically rolling back on error when auto commit is off.

Learn how to initialize and use get result batches to divide a Snowflake query into multiple batches, distribute fetching and processing across workers in parallel, and manage subset data references.

Learn to inspect and materialize Snowflake get result batches, select a batch and chunk, and use properties like compressed size and uncompressed size to gauge data size.

Fetch pandas batches demonstrates returning Snowflake query results as pandas dataframes in batches, using an iterator to iterate over each batch and assemble the full dataset.

Learn how to convert an arrow result batch back to a pandas dataframe or an arrow table using two dedicated methods, with a quick practical example.

Leverage the execute stream method to run a file of SQL statements sequentially, optionally removing comments, returning a cursor for results and error handling in Snowflake ELT workflows.

Explore dictionary cursor usage in Snowflake, querying data by column name or index, and managing connections, transactions, and exceptions in an ELT workflow with Airflow, Python, and Talend.

Copy data from S3 to Snowflake using a Python program that builds copy commands from a parameter file. It captures rejects and audits each load with a copy audit table.

Submit copy commands in parallel from s3 to snowflake using async execution, monitor status, and audit rejected records for each copy command.

Execute elt queries on Snowflake using Python to run insert statements in parallel or sequentially, while auditing with query history and an execute stream method.

Schedule an Airflow job to copy data from history into Snowflake and process it with SQL queries, using a parameter-based copy command and two tasks for copy and processing.

Explore Talend as an open source etl tool and its data integration, data preparation, governance, and big data capabilities, including cloud data fabric, Gartner leadership, and industry adoption.

Download and unzip the talend zip, start Talend Open Studio, and create the Snowflake project in the workspace, following the issues document and java notes.

Explore the Talend workspace for Snowflake ELT workflows, including job designs, variables, routines, templates, and metadata connections; learn drag-and-drop components, run, document, and export capabilities.

Set up the Talend studio and create a first job. Configure a Snowflake elt pipeline that loads a local employee file after defining the input schema.

Load two records into a Snowflake table using Talend’s file input and delimited input components, map columns with tMap, and write to Snowflake, verifying the load.

Use a separate key-value parameter file as input to the job; the job reads it and assigns values to variables, configuring the warehouse and database parameters dynamically.

Learn to parameterize Snowflake connection details in a Talend ELT workflow by moving hardcoded values to a repository context and context group, enabling externalized configuration for future jobs.

Read variable values from a parameter file and populate job variables from a connection file, then manage sub job dependencies to load records into Snowflake.

Build and run a Snowflake elt job from the command line, parameterizing the connection context. Generate executable files for Windows and Linux and verify data loading.

Schedule and run a talent job with Airflow by unzipping the build and loading data into a table using Python and Bash operators. In production, GitHub and Jenkins deploy jobs.

Install talent, explore its components, create and run jobs with context variables, execute and schedule jobs via the command line, and export talent jobs as portable jar files.

Learn to copy and process data in Snowflake with reusable components, using Java, Python, Talend, and Airflow, and build end-to-end ELT workflows with dashboards to monitor daily loads.

A non-technical analyst requests a simple, automated method to load files from S3 into Snowflake without manual copy commands, while the developer ensures command sequencing, cost auditing, and failure tracking.

Demonstrates copying data from S3 to Snowflake with a jar app, using connection and job parameter files to specify table, stage, file format, and rejects threshold.

Execute a predefined set of 50 analysis queries on Snowflake without a data engineer, with auditable costs and restartable failure handling in an ELT workflow using Airflow, Python, and Talend.

Explore how an elt app demo uses Snowflake connection details and job parameters with GitHub stored queries to load data into a control table and execute the queries.

The analyst passes the activity to the operations team, who will tag and schedule the job as an Airflow task after adhering to minimum and code standards.

Develop and schedule dag workflows in Airflow for copy and query jobs in Snowflake using bash operators, parameter files, and default arguments, while identifying hardcoded patterns for generalization.

Develop generic dag code for Snowflake ELT workflows by standardizing job-scoped parameter folders, naming tasks like copy_task and easier_task, and replacing hardcoded commands with reusable python-driven commands.

Learn to build generic DAG code in Airflow using a Python-based task builder, replacing bash operators with functions, and balance generalization with default arguments to create flexible workflows.