![]() ![]() As the world becomes more integrated with Internet of Things devices, real-time support is becoming increasingly important. ![]() A data lakehouse is built to better support this type of real-time ingestion compared to a standard data warehouse. Many data sources use real-time streaming directly from devices. The ability to separate compute from storage resources makes it easy to scale storage as necessary. Using open and standardized storage formats means that data from curated data sources have a significant head start in being able to work together and be ready for analytics or reporting. These are brought into a data lakehouse as a means of rapidly preparing data, allowing data from curated sources to naturally work together and be prepared for further analytics and business intelligence (BI) tools. Data management featuresĪ data warehouse typically offers data management features such as data cleansing, ETL, and schema enforcement. A data lakehouse offers many pieces that are familiar from historical data lake and data warehouse concepts, but in a way that merges them into something new and more effective for today’s digital world. With an understanding of a data lakehouse’s general concept, let’s look a little deeper at the specific elements involved. In a way, data lakehouses are data warehouses-which conceptually originated in the early 1980s-rebooted for our modern data-driven world. By providing the space to collect from curated data sources while using tools and features that prepare the data for business use, a data lakehouse accelerates processes. The result creates a data repository that integrates the affordable, unstructured collection of data lakes and the robust preparedness of a data warehouse. This means data can be easily moved between the low-cost and flexible storage of a data lake over to a data warehouse and vice versa, providing easy access to a data warehouse’s management tools for implementing schema and governance, often powered by machine learning and artificial intelligence for data cleansing. So, how does a data lakehouse combine these two ideas? In general, a data lakehouse removes the silo walls between a data lake and a data warehouse. A data warehouse typically includes data management features such as data cleansing and extract/load/transform (ETL). This data is typically queried by business users, who use the prepared data in analytics tools for reporting and projections. Data warehouse (the “house” in lakehouse): A data warehouse is a different kind of storage repository from a data lake in that a data warehouse stores processed and structured data, curated for a specific purpose, and stored in a specified format.Analysts believe the company's adjusted diluted EPS will compound at 10.1% annually through the next five years. This enabled the company's adjusted diluted EPS to grow faster than its revenue for the quarter.ĭomino's plans to open more stores worldwide and keep repurchasing shares at opportune times should continue to be a recipe for success. However, a 2.6% reduction in Domino's weighted average diluted share count more than offset this dip in profitability. A higher growth rate (5.2%) in the company's cost of sales compared to revenue brought pressure on its profitability, leading the net margin to decrease by 20 basis points to 11.4% in the quarter. Along with a 5.5% growth rate in the store count to almost 19,900 over the year-ago period, this explains Domino's decent top-line growth rate for the quarter.ĭomino's non-GAAP (adjusted) diluted earnings per share (EPS) increased by 4.2% year over year to $4.43 during the fourth quarter. These results prove that with inflation remaining high, cost-conscious customers were still appetized by both their love of pizza and the value proposition of the company's products. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |