In addition to our data lake, other source systems comprise relational LOB applications, Excel workbooks, other file-based sources, and Master Data Management MDM and custom data repositories. MDM repositories allow us to manage our master data to ensure authoritative, standardized, and validated versions of data. On a periodic basis, and according to the rhythms of the business, data is ingested from source systems and loaded into the data warehouse.
It could be once a day or at more frequent intervals. Data ingestion is concerned with extracting, transforming, and loading data. Or, perhaps the other way round: extracting, loading, and then transforming data. The difference comes down to where the transformation takes place. Transformations are applied to cleanse, conform, integrate, and standardize data. For more information, see Extract, transform, and load ETL. Ultimately, the goal is to load the right data into your enterprise model as quickly and efficiently as possible.
The services is used to schedule and orchestrate data validations, transformations, and bulk loads from external source systems into our data lake. It's managed by custom frameworks to process data in parallel and at scale. In addition, comprehensive logging is undertaken to support troubleshooting, performance monitoring, and to trigger alert notifications when specific conditions are met. Meanwhile, Azure Databricks —an Apache Spark-based analytics platforms optimized for the Azure cloud services platform—performs transformations specifically for data science.
It also builds and executes ML models using Python notebooks. Scores from these ML models are loaded into the data warehouse to integrate predictions with enterprise applications and reports. Because Azure Databricks accesses the data lake files directly, it eliminates or minimizes the need to copy or acquire data. We developed an ingestion framework as a set of configuration tables and procedures. It supports a data-driven approach to acquiring large volumes of data at high speed and with minimal code.
In short, this framework simplifies the process of data acquisition to load the data warehouse. The framework depends on configuration tables that store data source and data destination-related information such as source type, server, database, schema, and table-related details. Instead, procedures are written in the language of our choice to create ADF pipelines that are dynamically generated and executed at run time.
So, data acquisition becomes a configuration exercise that's easily operationalized. The ingestion framework was designed to simplify the process of handling upstream source schema changes, too.
It's easy to update configuration data—manually or automatically, when schema changes are detected to acquire newly added attributes in the source system. We developed an orchestration framework to operationalize and orchestrate our data pipelines. It uses a data-driven design that depends on a set of configuration tables. These tables store metadata describing pipeline dependencies and how to map source data to target data structures.
The investment in developing this adaptive framework has since paid for itself; there's no longer a requirement to hard-code each data movement. A data lake can store large volumes of raw data for later use along with staging data transformations.
It stores raw data alongside staged data and production-ready data. It provides a highly scalable and cost-effective data lake solution for big data analytics. Combining the power of a high-performance file system with massive scale, it's optimized for data analytic workloads, accelerating time to insight.
ADLS Gen2 provides the best of two worlds: it's BLOB storage and a high-performance file system namespace, which we configure with fine-grained access permissions. Refined data is then stored in a relational database to deliver a high-performance, highly scalable data store for enterprise models, with security, governance, and manageability. At the reporting layer, business services consume enterprise data sourced from the data warehouse. They also access data directly in the data lake for ad hoc analysis or data science tasks.
Fine-grained permissions are enforced at all layers: in the data lake, enterprise models, and BI semantic models. The permissions ensure data consumers can only see the data they have rights to access.
Some reporting and ad hoc analysis is done in Excel—particularly for financial reporting. This browser is no longer supported. Download Microsoft Edge More info.
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