Break free from data silos. LakeHouseIn combines the flexibility of a data lake with the structure of a data warehouse. Analyze all your data, from structured to unstructured, at once. Get the insights you need, faster. Consolidate and analyze all your data – no matter the format. Streamline your ML/AI/MLOps workflows. LakeHouseIn empowers data-driven decision making.
This data is often stored in isolated databases across different departments, systems and formats. Decision makers and business units face delays in accessing information in these isolated environments.
This delay not only affects the speed of decision-making, but also reduces the potential to extract value from data. Because the effect of the measures taken will decrease as we move away from the moment of the incident. Additionally, traditional reporting tools report information from one day in advance at the earliest. The speed and competitive conditions of today’s world require access to the most up-to-date information as soon as possible.
LakeHouseIn allows you to perform data analysis and AI/ML studies by combining different data sources either on-site or in a central location, using powerful open source tools such as Apache Spark, Trino and MinIO.
Support for ACID transactions provides consistency for multiple parties to read or write data simultaneously, often using SQL.
Flexibility in schema management and schema changes with metadata.
Direct connection with Business Intelligence tools, offering reporting and analysis.
It supports both streaming and batch workloads.
Storage and computing resources are separate. Computational resources can be increased, decreased, or completely turned off as needed.
Comfortable AI/ML working environment and easy MLOps.
Parquet and ORC, image, video, audio, semi-structured, text.
It can transfer the source data to the analytical environment by passing it through the SQL-based ETL process without the need for another ETL tool.
By combining the flexibility of data lakes with the data management and ACID features of data warehouses, it makes it easy to store, manage and analyze all types of data in a single system.
Its ability to independently scale storage and computing resources allows it to reduce or completely turn off the computing resource when it is not needed. It also saves storage space with modern data formats and compression.
Make it easy to experiment with new data and analytics workloads. Has the ability to adapt to change. Thus, it helps respond faster to changing business needs.
It enables faster insights from data by providing interactive query performance on all types of data. Does not make you wait for days to access information.
The needs of modern data-oriented companies such as data analysis, ML/AI/MLOps, streaming, batch processing and workflow orchestration are all in one.
