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Building streaming data pipelines on Google Cloud

Streaming analytics pipelines are designed to process and analyze data in real-time as it flows through the system. These pipelines are commonly used in various industries, including finance, healthcare, e-commerce, and IoT, to gain insights, detect anomalies, make data-driven decisions, and trigger actions in response to events or patterns in the data.

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Here's an overview of the architecture of streaming analytics pipelines:

Data Ingestion:

Data sources: Data can be ingested from various sources, such as sensors, logs, social media feeds, databases, and external APIs. These sources continuously produce or send data. - GCP Data Engineer Online Training

Ingestion layer: This layer collects and ingests data from sources into the pipeline. Common technologies for data ingestion include Apache Kafka, Apache Pulsar, Amazon Kinesis, or custom-built solutions.

Data Transformation:

Data preprocessing: Raw data often needs to be cleaned, transformed, and enriched before analysis. This step may involve data validation, normalization, filtering, and enrichment with reference data. - GCP Training in Hyderabad

Stream processing: Stream processing frameworks like Apache Flink, Apache Kafka Streams, Apache Spark Streaming, or AWS Lambda can be used to process data in real-time. These frameworks allow you to apply operations like map, filter, join, and aggregate to the incoming data streams.Google Cloud Data Engineer Training

Real-Time Analytics:

Analytical models: Streaming analytics pipelines may include machine learning models, rules engines, or complex event processing (CEP) engines to perform real-time analysis on the data. These models can detect patterns, anomalies, correlations, and trends. - GCP Data Engineer Training in Ameerpet

Stateful processing: Some analytics scenarios require maintaining state across incoming data streams, such as sessionization or tracking the progress of a process over time.

Event Storage:

Data storage: Processed data or important events may be stored in a real-time database or data store for later retrieval and analysis. Common choices include Apache Cassandra, Elasticsearch, or cloud-based databases like Amazon DynamoDB or Azure Cosmos DB. - GCP Online Training

Long-term storage: Depending on compliance and historical analysis requirements, a portion of the data may also be stored in a data lake or data warehouse for further analysis and reporting.

Visualization and Monitoring:

Real-time dashboards: Tools like Grafana, Kibana, or custom dashboards can display real-time insights and trends to users.

Monitoring: Continuous monitoring of pipeline health, data quality, and performance is essential. Tools like Prometheus, Nagios, or custom monitoring solutions can be used.

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