What is Data Lakehouse, roadmap for a data-driven in 2021?

Dataumi explains Data Lakehouse is a Mix of Data-Lakes with Data-Warehouses.

A data lakehouse is amazing new, open data management paradigm that mixes the capabilities data lakes and data warehouses, enabling BI and ML on all data.

Data warehouses have an extended history in decision support and business intelligence applications, though weren't suited or were expensive for handling unstructured data, semi-structured data, and data with high variety, velocity, and volume.

Data lakes then emerged to handle data during a sort of formats on cheap storage for data science and machine learning, though lacked critical features from the planet of data warehouses: unlucky, does not support transactions, also, does not enforce data quality, and their lack of consistency/isolation makes it almost impossible to avail both appends and reads, and batch and streaming jobs.

Data teams consequently stitch these systems together to enable BI and ML across data silos in both these systems, This causes duplicate data, extra infrastructure cost, and security challenges.

Data lakehouses are enabled by an open system design: implementing similar data structures and data management features to those existing in data warehouse, directly on the type of low-cost storage used for data lakes. Merging them together into one system means data teams can move faster as they're ready to use data without having to access multiple systems. Data lakehouses also make sure that teams have the foremost complete and up to date data available for data science, machine learning, and business analytics projects.

A data lakehouse is a data solution concept that combines elements of the data warehouse with those of the data lake. Data lakehouses implement data warehouses’ data structures and management features for data lakes, which are typically more cost-effective for data storage. Data lakehouses are useful to data scientists as they enable machine learning and business intelligence.

FEATURES OF A DATA LAKEHOUSE:

As a combination of data warehouses and data lakes, data lakehouses feature elements of both data platforms. Namely:

- Concurrent reading and writing of data

- Schema support with mechanisms for data governance

- Direct access to source data

- Separation of storage and compute resources

- Standardized storage formats

- Support for structured and semi-structured data types, including IoT data

- End-to-end streaming
DATA LAKEHOUSE VS DATA WAREHOUSE VS DATA LAKE:

Many businesses operate their data warehouses independently of their data lakes, leveraging data warehousing to derive valuable business insights and using data lakes for storage and data science. Some businesses combine their data lake with their data warehouses in a single data platform — either a data warehouse working in parallel with their data lake or a data warehouse embedded in their data lake — that serves data for business intelligence and data science. Some businesses even add data marts to their data storage stacks, as well.

On the other hand, a data lakehouse serves as a single platform for data warehousing and data lake.

ADVANTAGES OF a data LAKEHOUSE:

The ability to derive intelligence from unstructured data (text, images, video, audio) makes handling these sorts of data critical for businesses. Traditionally, though, data warehouses weren't optimized for these unstructured data types, making it necessary to simultaneously manage multiple systems – a data lake, several data warehouses, and other specialized systems. Maintaining various systems are often costly and even delay your ability to access timely data insights.

- A single data lakehouse has several advantages over a multiple-solution system, including:

- Less time and effort administrating

- Simplified schema and data governance

- Reduced data movement and redundancy

- Direct access to data for analysis tools

- Cost-effective data storage

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