Understand the stages for processing big data by using Azure Data Lake Store
Azure Data Lake Storage Gen2 plays a fundamental role in a wide range of big data architectures. These architectures can involve the creation of:
- A modern data warehouse.
- Advanced analytics against big data.
- A real-time analytical solution.
There are four stages for processing big data solutions that are common to all architectures:
Ingestion - The ingestion phase identifies the technology and processes that are used to acquire the source data. This data can come from files, logs, and other types of unstructured data that must be put into the Data Lake Store. The technology that is used will vary depending on the frequency that the data is transferred. For example, for batch movement of data, Azure Data Factory may be the most appropriate technology to use. For real-time ingestion of data, Apache Kafka for HDInsight or Stream Analytics may be an appropriate technology to use.
Store - The store phase identifies where the ingested data should be placed. In this case, we're using Azure Data Lake Storage Gen2.
Prep and train - The prep and train phase identifies the technologies that are used to perform data preparation and model training and scoring for data science solutions. The common technologies that are used in this phase are Azure Databricks, Azure HDInsight or Azure Machine Learning Services.
Model and serve - Finally, the model and serve phase involves the technologies that will present the data to users. These can include visualization tools such as Power BI, or other data stores such as Azure Synapse Analytics, Azure Cosmos DB, Azure SQL Database, or Azure Analysis Services. Often, a combination of these technologies will be used depending on the business requirements.