Modern Data Stack: The Key to Data Success

The growing data challenge

Organisations today face an unprecedented data challenge. Data generation is increasing rapidly and is set to reach 181 zettabytes in three years. However, most companies struggle to extract value from this vast data. Challenges include data lakes becoming swamps, outdated systems, and unscalable stacks. These problems make data insights difficult to achieve. As a result, many rely on instincts instead of data. This is not enough in a data-driven world. A modern data stack is crucial to harness data effectively.

The following image illustrates key data statistics, showing how most companies face challenges in realising data value.

The evolving data landscape

Previously, data platforms were simple. They typically involved a single database for basic storage and queries. However, the exponential growth of data has made this approach obsolete. The modern data landscape is complex and rapidly changing, with new tools constantly emerging.

Look at the comprehensive image below to grasp the growing number of data tools in the market

It just shows that there are a ton of data tools out there and that the number of tools available is still increasing every year. Fun fact, there’s a game called “Big Data or Pokemon” that challenges you to distinguish between big data products and Pokemon names. This shows just how overwhelming the options have become. To navigate this landscape, we need a strategic approach for a modern data architecture.

The Modern Data Architecture

Building a modern data architecture requires understanding various components. The image below presents a high-level architecture schema, outlining data ingestion, transformation, governance, and more.

The lake house solution

A lake house architecture merges the best of data lakes and warehouses. It offers flexibility, scalability, and consistency. It combines blob storage, like Google Cloud Storage, with the power of BigQuery. Big Lake enhances this by allowing BigQuery functionalities on structured blob storage data. This approach prevents redundant storage and simplifies access.

Simplifying data ingestion

Data ingestion can be tedious and costly. Companies have three main options. First, they can use Google Cloud tools like BigQuery Transfer Service. Second, they can use managed services like FiveTran for non-GCP sources. Third, they can opt for custom solutions for niche cases. The image below shows how different paths for data ingestion compare. Simplifying data ingestion reduces development and maintenance burdens while balancing costs.

Streamlining data transformations

Raw data often needs transformation to be useful. Self-service transformations empower teams to prepare data quickly. This is crucial for BI tools and AI/ML applications. Dataform and dbt are popular for these tasks. Dataform works well with Google Stack. On the other hand, dbt suits teams with Python knowledge. Both tools ensure quality through data testing, version control, and lineage.

The power of Looker for BI and ML

BI teams face common problems. They either become a bottleneck or experience scattered, duplicated efforts across departments. This happens when analytics tools are too complex. Looker helps overcome these issues by connecting analysts directly to data layers, as shown in the image below. It supports self-service BI, allowing analysts to create dashboards without needing to navigate complex data structures.

The power of Vertex AI

AI projects often face challenges when moving from development to production. Vertex AI solves this by allowing teams to use pre-trained Google models or customise them. It eliminates the need for platform migration. This simplifies AI deployment and management, making it a game-changer for a modern data stack.

Orchestration and data governance

Orchestration ensures smooth data operations. It runs processes in the right order and prevents errors. Workflow and Airflow are two main tools. Workflow is cost-effective and lightweight. Airflow handles complex pipelines but comes with higher costs. Data governance is also crucial. It ensures data quality, security, and accessibility. The image below shows that data governance is not only about tools but also about the people involved. Effective governance includes access control, data lifecycle management, and lineage tracking.

The importance of cost control and observability

Controlling costs in data analytics is essential. Data queries can become expensive quickly. Budget alerts and proactive monitoring help manage expenses. Observability tracks data usage and performance. It provides insights that help organisations optimise operations and prevent cost overruns.

Conclusion: Building a modern data stack

Creating a modern data stack means addressing BI limitations, adopting AI solutions like Vertex AI, and ensuring strong governance. The right tools and practices empower teams, simplify workflows, and maximise data value. A strategic approach with effective tools enables companies to overcome challenges and thrive.

This image underscores that building a robust modern data stack is not just about tools; it’s also about people.