Best Data Lake Solutions for Microsoft Power BI

Discover insights to boost sales growth with code-free, fully automated data pipelines to data lakes and cloud warehouses. Flexible, standards-based platform that unifies sales and marketing data to automate insights and smarter growth. Say goodbye to manual data downloads that are expensive and messy. You will always know exactly what you'll be charged and only pay what you actually use. Access to data-ready data is a great way to fuel your tools. We only work with official APIs as certified developers. Data pipelines from well-known sources are easy to use. These data pipelines are pre-built, pre-transformed and ready to go. Unlock data from Amazon Vendor Central and Amazon Seller Central, Instagram Stories. Teams can quickly and economically realize the value of their data with code-free data ingestion and transformation. Databricks, Amazon Redshift and other trusted data destinations like Databricks or Amazon Redshift ensure that data is always protected.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

AnalyticsCreator lets you extend and adjust an existing DWH. It is easy to build a solid foundation. The reverse engineering method of AnalyticsCreator allows you to integrate code from an existing DWH app into AC. So, more layers/areas are included in the automation. This will support the change process more extensively. The extension of an manually developed DWH with an ETL/ELT can quickly consume resources and time. Our experience and studies found on the internet have shown that the longer the lifecycle the higher the cost. You can use AnalyticsCreator to design your data model and generate a multitier data warehouse for your Power BI analytical application. The business logic is mapped at one place in AnalyticsCreator.

Azure Data Lake offers all the capabilities needed to make it easy to store and analyze data across all platforms and languages. It eliminates the complexity of ingesting, storing, and streaming data, making it easier to get up-and-running with interactive, batch, and streaming analytics. Azure Data Lake integrates with existing IT investments to simplify data management and governance. It can also seamlessly integrate with existing IT investments such as data warehouses and operational stores, allowing you to extend your current data applications. We have the experience of working with enterprise customers, running large-scale processing and analytics for Microsoft businesses such as Office 365, Microsoft Windows, Bing, Azure, Windows, Windows, and Microsoft Windows. Azure Data Lake solves many productivity and scaling issues that prevent you from maximizing the potential of your data.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.