Data Lakehouses for Real-time Intelligence: The Secret Weapon for Business 4.0
Industry 4.0 refers to the current era of industrialization i.e. the 4th era of digitization in the manufacturing sector which includes disruptive trends like the rise of data and it’s usage wrt connectivity, analytics, human-machine interaction, and real-time information. The need to solve this resulted in Business 4.0
Business 4.0 is the business model that utilizes new technologies like IoT, AI, ML, etc. The purpose of this is to bring the data together and start leveraging it cohesively. Though we can go on and on about the use cases and how data can be leveraged in Industry 4.0, we’ll take a step back and ask: Does your data foundation support all this?
There is an old joke in data management — that your entire code is based on a 1000- written in 2018. Don’t bring your data engineering practices down with increased data debt and legacy data practices. To support your business 4.0 practices you need an evolved data ingestion, storage, and management platform like Data Lakehouse.
What is Data Lakehouse?
Lakehouses combine the key benefits of data lakes i.e. low-cost storage in an open format accessible by a variety of systems and powerful management and optimization features from data warehouses.
In data lakehouses, the data storage is done in open formats like that of a data lake in Parquet, but a metadata layer on top of the data lake enables ACID transactions and other data warehouse querying capabilities while keeping data in table format. This system with the best of both worlds enables you to eliminate redundancies and costs associated with the combined architecture of data lake + data warehouse.
Also Read: How The Adoption Of Data Lake As A Service Is Transforming The Businesses
The data lakehouse architecture can be seen below:
What’s the need for data lakehouse?
There are a lot of reasons for this. But to understand this, we need to know what the current two-tier architecture is that companies that currently using. Companies usually have a data lake for storing the data — from which data is ETLed to another data warehouse for analysis. This ensures that both ML and BI capabilities are possible with the data. But some issues arise with it:
For example, the data lake and warehouse systems might have different semantics in their supported data types, SQL dialects, etc; data may be stored with different schemas in the lake and the warehouse (e.g., denormalized in one); and the increased number of ETL/ELT jobs, spanning multiple systems, increases the probability of failures and bugs.
None of the leading machine learning systems, such as TensorFlow, PyTorch, and XGBoost, work well on top of warehouses — they need APIs and complex non-SQL code to process data — unlike Business Intelligence (BI) which extracts a small amount of data. This is usually why Data Lakes are the preferred storage format for data scientists, as the data is easily extracted from it.
In addition, the added cost of data duplication and the cost of double ETL and ELT transfers from the source to the data lake and data warehouse.
There is an additional need for increased scalability to accommodate:
- Both AI & BI projects together
- Support for diverse workloads
- Support for diverse datasets from unstructured to structured data
- Transaction support while having open data storage
- Schema enforcement with data integrity
- And support streaming data
You can study the differences between Data warehouse, Data lakehouse, and data lake in detail along with the data lakehouse architecture in detail here.
Understanding the difference between Data warehouse and Data Lakehouse
While data warehouses excel at structured data and SQL analytics, they struggle with unstructured data, real-time data ingestion, and advanced analytics, like machine learning. On the other hand, while data lakes are good at low-cost storage, they don’t support data management capabilities and ensure effective data quality.
Therefore, the data lakehouse overcomes these limitations by incorporating key features like open data formats (e.g., Parquet), a metadata layer for ACID transactions, and direct access to data files from a variety of tools.
Enabling real-time intelligence with Data Lakehouse
In the current era of instant gratification and ever-increasing streaming tools, waiting hours or days for data processing is no longer acceptable. Data lakehouses excel at processing vast amounts of data in real time, allowing businesses to make split-second decisions based on the most current information.
Lakehouses Architecture supports streaming data by having 5 layers namely:
- Data Ingestion layer
- Stream processing layer
- Data Storage layer
- Query Engine
- Consumption layer
Be it Microsoft Azure Data Lakehouse OneLake Databricks Data Lakehouse, or the lakehouse of any other tool, you’ll need to choose the best one based on your data analytics needs, cost, scalability, type of data, and more.
