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]]>The timely loading of real-time data from your on-site or cloud-based mission-critical operational systems to your cloud-based analytical systems is assured by a continuous streaming ETL solution. The data loaded for making crucial operational decisions should be reliable due to continuous data flow. By supplying an efficient, end-to-end data integration between the source and the target systems, Striim can guarantee the dependability of the stream ETL solutions. To ensure data reliability and send it to the target systems, the data from the source can be transformed in the real-time data pipeline. Striim applications can be created for a variety of use cases.
A power company called Glitre Energi manages the power grid, retails electricity, and offers broadband services. About 90,000 people receive electricity from Glitre Energi. The organization oversees the power lines that pass through heavily populated areas.
Also Read: Use Cases for a Unified Data Integration and Streaming Platform like Striim
Getting the most unique identifier from the target table is made possible by a memory-based cache of non-real-time historical or reference data that was obtained from an external source.
External Cache The need for data prompts Striim to query an external database. In order to determine whether the incoming data is present in the target table already or not, Striim queries the same data when it joins with real-time data.
By limiting the data set by a specific number of events, period, or both, Windows will aggregate, join, or perform calculations on the data. This aids in bringing the target database data and real-time data together in one location where the downstream pipeline can carry out the transformations.
A continuous query that can be used to filter, aggregate, join, enrich, and transform the events specifies the logic of an application. A query facilitates the logic in the data pipeline and the combining of data from various sources.
Read About Our Success: How we were able to assist one of the biggest manufacturing companies involved setting up an ETL process using PySpark to move sales data from a MySQL on-premises database, which was then obtained from several different ERP systems to Redshift on the AWS cloud.
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The use case’s high-level representation is shown in the image below:
Using continuous query components has made it simple to compare event data in real-time load to reach decisions. With the aid of windows and cache components, efficient data that must be retrieved from external sources has been planned out very well. The beauty of Striim allows data to be joined wherever it is needed and the desired output to be obtained, assisting Glitre Energi in achieving the normalization of their metering events in their relational systems.
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Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.
The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:
With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.
The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;
Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.
DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.
The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.
DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.
Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.
The concepts used in DLT include:
Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets
Pipeline Setting: Pipeline settings can define configurations such as;
Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.
Pipeline Modes: Delta Live provides two modes for development:
Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.
Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.
Editions: DLT comes in various editions to suit the different needs of the customers such as:
Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.
Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.
Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.
Maintenance tasks are performed on tables every 24 hours by Delta Live Tables, which improves query outcomes. It also removes older versions of tables and improves cost-effectiveness.
No, this is not possible. Each table should be defined once. UNION can be used to combine various inputs to create a table.
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