A data platform is a technology infrastructure that enables an organisation to store, manage, process, and analyse large volumes of data from various sources. It typically consists of hardware, software, and networking components that work together to support data management and analysis.
A data lake is a centralized repository that allows organizations to store all their structured and unstructured data, at any scale, and in its raw format. It provides a way to store and manage massive amounts of data without having to define the schema in advance, which makes it more flexible than traditional data warehouses.
Clickstream analytics is the process of collecting, analyzing, and interpreting data related to a user's interactions with a website or application. This data includes the sequence of pages or screens visited, the duration of each visit, the actions taken, and other contextual information such as the user's device type and location.
A Data Platform is an infrastructure that enables organizations to build scalable data pipelines.
IntegrationIntegrate data from various sources, including databases, cloud services, and third-party applications. It includes tools for data ingestion, transformation, and normalisation.
FlexibilityCan accommodate various types of data and support multiple data processing and analysis tools.
ScalabilityOur solutions handle increasing volumes of data and provide high performance and scalability to support business growth.
Cost EffectivenessThe workloads can run distributed on relatively smaller instances, so they run much more faster and don’t rely on expensive hardware.
AutomationThe platform is built with infrastructure-as-code principles. The workloads can be integrated to CI/CD pipelines.
Google Analytics Integration
- Google Analytics has a direct integration with BigQuery
- Supports daily batches and/or streaming
Google Tag Manager Integration
- GTM supports BigQuery events
- Events can be written directly to BigQuery
Custom Clickstream Analytics
- Custom events from various applications can be collected
- Highly scalable and durable architecture
- Pre-transformations can be implemented in Cloud Functions
- Data will stream thru Pub/Sub, can be used in real-time dashboards
- Streaming data will be written into Big Query
Relational Database Integration
- Near Real-time or batch data synchronisation
- Replicating database as is in Data Lake
- Cloud native or Generic solutions are available
Other Data Sources
- Other data sources can be integrated into the Data Lake
- E.g. Ethereum data can be listened and synced in Big Query via custom apps
- Data will also be available for real-time processing
- Certain events can be tracked and can raise some alerts
- Big Query will store both Raw Data and Data Marts
- Cloud Composer will orchestrate the data pipelines
- dbt-core will be responsible for SQL transformations and building the models
- Data Marts will be available in Big Query
- BI Tool will use Data Marts
Let's have a quick chat to understand your requirements and figure out how we can help you.
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