The Modern Data Stack: Explained For Friends
Tags: datascience, database, analytics
Published: 2023-06-03 12:00:00

Hey there! So, you've heard the term "modern data stack" floating around and are curious about what it means? Fantastic! 

The modern data stack, in simple terms, is the combination of tools and technologies designed to manage, manipulate, analyze, and make sense of large volumes of data. This might seem a bit intimidating initially, but don't worry. Imagine the modern data stack as a well-organized kitchen where each tool has a specific role in creating a fabulous meal. This 'meal,' in our case, is valuable insights derived from data that drive critical business decisions.

## Step 1: Data Ingestion and ETL Tools

Imagine you're planning a dinner. You'd start by gathering ingredients, right? 

That's precisely the function of data ingestion tools and ETL (Extract, Transform, Load) processes. They extract data from various sources, transform it into a usable format, and load it into a data warehouse.

In our modern data stack kitchen, tools like [Fivetran](https://www.fivetran.com/), [Stitch](https://www.stitchdata.com/), and [Apache NiFi](https://nifi.apache.org/) are our diligent sous chefs, gathering ingredients (data) from multiple stores (sources). There's a growing trend towards ELT rather than ETL, where data is loaded in its raw form and transformed later. This switch in the order allows for greater flexibility and agility, especially when dealing with voluminous data. 

## Step 2: Data Warehousing

Once the ingredients are gathered, you need a pantry or fridge to store them. That's what a data warehouse does. It's a central repository where the cleaned and processed data is stored and organized. 

Think of [Google BigQuery](https://cloud.google.com/bigquery), [Amazon Redshift](https://aws.amazon.com/redshift/), [Snowflake](https://www.snowflake.com/en/), and [Microsoft Azure Synapse Analytics](https://azure.microsoft.com/en-us/products/synapse-analytics/) as our cool, spacious, and well-organized fridges keeping our ingredients fresh and accessible. 

## Step 3: Data Lake

Now, not all ingredients can be neatly stored in a fridge. We also need a storage space for bulkier, raw items like sacks of rice or flour. Enter the data lake, a storage repository for large volumes of raw data.

In the data world, we have [Amazon S3](https://aws.amazon.com/s3/), [Google Cloud Storage](https://cloud.google.com/storage), and [Azure Data Lake Storage](https://azure.microsoft.com/en-us/products/storage/data-lake-storage) serving as our storage sheds, offering plenty of space for our vast amounts of raw data.

## Step 4: Data Processing

This is where the cooking happens. Just like you'd chop, blend, cook, and mix ingredients to create a dish, data processing tools transform and manipulate data to generate insights.

[Apache Spark](https://spark.apache.org/), [Apache Beam](https://beam.apache.org/), and [Google Cloud Dataflow](https://cloud.google.com/dataflow) are our modern-day kitchen gadgets, expertly processing our raw data into something meaningful.

## Step 5: Data Cataloging

Now, a well-organized kitchen has labels on its spice jars and shelves, doesn't it? Similarly, data cataloging tools help organize and locate data within vast datasets. It's as if we have [Alation](https://www.alation.com/), [Amundsen](https://www.amundsen.io/), and [Google Cloud Data Catalog](https://cloud.google.com/data-catalog/docs/concepts/overview) putting handy labels on our data, helping us find what we need quickly.

## Step 6: Business Intelligence (BI) and Data Visualization Tools

After all that cooking, it's time to serve our dish on a beautiful platter. That's what BI and data visualization tools do – they present data in an easily digestible, visual format.

Just like a well-arranged dish is more appetizing, [Tableau](https://www.tableau.com/), [Looker](https://cloud.google.com/looker), [PowerBI](https://powerbi.microsoft.com/en-us/), and [Google Data Studio](https://datastudio.withgoogle.com/) help present our insights in an appealing, easy-to-understand manner, making decision-making a breeze.

## Step 7: Data Science Platforms

For those of us who love to experiment with complex recipes, we need an advanced platform to experiment and innovate. Data science platforms offer the tools and environments for complex data analysis, model building, and deploying machine learning algorithms.

Our laboratory-grade kitchen gadgets in this case are [Databricks](https://www.databricks.com/), [Jupyter Notebooks](https://jupyter.org/), [Google Colab](https://colab.research.google.com/), and [Kaggle](https://www.kaggle.com/). These allow us to explore, experiment, and create with our data.

## Step 8: Orchestration Tools

Of course, all these processes need to be well-managed and coordinated, like a well-oiled kitchen machine. That's where orchestration tools come in, helping to manage, schedule, and monitor data pipelines.

[Apache Airflow](https://airflow.apache.org/), [Google Cloud Composer](https://cloud.google.com/composer), and [Prefect](https://www.prefect.io/) are our indispensable kitchen managers, ensuring everything runs smoothly and efficiently.

## Step 9: Data Governance and Security

Last but definitely not least, just like we ensure our kitchen meets food safety regulations and our ingredients are high quality, data governance and security tools maintain data quality, protect sensitive data, and ensure compliance with regulations. Our tools for this critical task include [Immuta](https://www.immuta.com/), [Privacera](https://privacera.com/), and [Okera](https://www.okera.com/).

So, there you have it, my friend - a simplified tour of the modern data stack! It's an ever-evolving world out there, with new tools and technologies constantly emerging, but this should give you a good grounding. 

Happy data exploring!