Build a modern cloud data platform for ALL data: Today, data is easier to access than ever, but the volume, variety, and ways it is used has increased greatly. Machine learning (ML) and Analytics requires massive amounts of data across multiple data sources – thereby enslaving the data management team.
Lakehouse combines the best of data warehouses and data lakes – simplifying enterprise data in the cloud, accelerating innovation, and increasing productivity up to 20 percent.
Why You Need a Lakehouse Approach??? Realize many benefits identified with this approach:
✪ Enable a single combined cloud data platform: Lakehouse stores ALL data in a common format across the whole architecture.
✪ Unifies data warehousing and machine learning (ML): One platform for data warehousing and ML that supports all types AND frequency of data.
✪ Increases data team efficiency: Lakehouses are enabled by a new system design that implements similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. Merging them into a single system means that ML teams can move faster because they are able to use data without needing to access multiple systems.
✪ Reduces Costs: With this approach, you have one system for data warehouse and ML. You do not need multiple systems for different analytics use cases. Store data on cheap object storage (Amazon S3, Azure Blob, etc.).
✪ Simplifies data governance: A lakehouse can eliminate the operational overhead of managing data governance on multiple tools.
✪ Supports data versioning: Uniqueness can be used by data consumers to determine whether data has changed (and how) over time and specifically which version of a data set they are working with.
✪ Simplifies ETL jobs: With the data warehousing technique, the data has to be loaded into the data warehouse to query or to perform analysis. But by using the lakehouse approach, the ETL process is eliminated by connecting the query engine directly to the data lake.
✪ Removes data redundancy: Lakehouse approach removes data redundancy by using a single tool to process raw data. Data redundancy happens when you have data on multiple tools and platforms such as cleaned data on data warehouse for processing, some meta-data on business intelligence (BI) tools, and temporary data on ETL tools. Enables direct data access: Data teams can use a query engine to query the data directly from raw data, giving them the power to build their transformation logic and cleaning techniques after understanding basic statistical insights and quality of the raw data.
✪ Connects directly to BI tools: Lakehouses enables tools, such as Apache Drill and supports the direct connection to popular BI tools like Tableau, PowerBI, etc.
✪ Handles security: Data related security challenges are easier to handle with a simplified data flow and a single source of truth approach.
Here below is the Databricks Lakehouse Platform that provides a unified environment for data, analytics, and machine learning work. Visualization can be an integral part of these different activities.
Lakehouse combines the best of data warehouses and data lakes – simplifying enterprise data in the cloud, accelerating innovation, and increasing productivity up to 20 percent.
Why You Need a Lakehouse Approach??? Realize many benefits identified with this approach:
✪ Enable a single combined cloud data platform: Lakehouse stores ALL data in a common format across the whole architecture.
✪ Unifies data warehousing and machine learning (ML): One platform for data warehousing and ML that supports all types AND frequency of data.
✪ Increases data team efficiency: Lakehouses are enabled by a new system design that implements similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. Merging them into a single system means that ML teams can move faster because they are able to use data without needing to access multiple systems.
✪ Reduces Costs: With this approach, you have one system for data warehouse and ML. You do not need multiple systems for different analytics use cases. Store data on cheap object storage (Amazon S3, Azure Blob, etc.).
✪ Simplifies data governance: A lakehouse can eliminate the operational overhead of managing data governance on multiple tools.
✪ Supports data versioning: Uniqueness can be used by data consumers to determine whether data has changed (and how) over time and specifically which version of a data set they are working with.
✪ Simplifies ETL jobs: With the data warehousing technique, the data has to be loaded into the data warehouse to query or to perform analysis. But by using the lakehouse approach, the ETL process is eliminated by connecting the query engine directly to the data lake.
✪ Removes data redundancy: Lakehouse approach removes data redundancy by using a single tool to process raw data. Data redundancy happens when you have data on multiple tools and platforms such as cleaned data on data warehouse for processing, some meta-data on business intelligence (BI) tools, and temporary data on ETL tools. Enables direct data access: Data teams can use a query engine to query the data directly from raw data, giving them the power to build their transformation logic and cleaning techniques after understanding basic statistical insights and quality of the raw data.
✪ Connects directly to BI tools: Lakehouses enables tools, such as Apache Drill and supports the direct connection to popular BI tools like Tableau, PowerBI, etc.
✪ Handles security: Data related security challenges are easier to handle with a simplified data flow and a single source of truth approach.
Here below is the Databricks Lakehouse Platform that provides a unified environment for data, analytics, and machine learning work. Visualization can be an integral part of these different activities.