Skip to main content

Lakehouse Concept | Data Warehouse | Data Lake

Lakehouse concept:

In the context of data management and analytics, a "lakehouse" refers to a modern data architecture that combines the capabilities of data lakes and data warehouses. It aims to address the limitations and challenges associated with these traditional data storage and processing approaches.

A data lakehouse provides a unified and scalable platform for storing, managing, and analyzing large volumes of structured and unstructured data. It incorporates the following key features:

Data Storage: Similar to a data lake, a lakehouse enables the storage of raw and unprocessed data in its native format. This includes structured data (e.g., relational databases, CSV files) and unstructured data (e.g., logs, sensor data, images). By using a common storage layer, such as a distributed file system, data can be ingested from various sources without requiring upfront schema design or transformation.


Ref.: https://www.databricks.com/wp-content/uploads/2020/01/data-lakehouse-new-1024x538.png

ACID Transactions: Unlike traditional data lakes, a lakehouse provides ACID (Atomicity, Consistency, Isolation, Durability) transactional guarantees. This means that data can be updated, deleted, and queried reliably, ensuring consistency and data integrity. ACID compliance enables the execution of complex analytics workflows and supports real-time and batch processing.

Schema Enforcement: A lakehouse allows for schema enforcement and schema evolution. It enables the definition of a schema upon data ingestion, ensuring that data adheres to a specific structure or schema. This feature makes it easier to maintain data quality, enforce governance policies, and enable self-service analytics.

Data Processing: A lakehouse incorporates data processing capabilities, typically using distributed processing frameworks like Apache Spark. This enables data transformation, cleansing, aggregation, and other data preparation tasks. The processing capabilities are integrated within the same platform, eliminating the need to move data between different systems.

Querying and Analytics: A lakehouse provides SQL-based querying capabilities, allowing users to perform ad-hoc and complex analytics directly on the stored data. It supports both batch processing and real-time streaming analytics, enabling organizations to derive insights and make data-driven decisions in a timely manner.

By combining the strengths of data lakes and data warehouses, a lakehouse architecture aims to provide a more flexible, scalable, and efficient approach to managing and analyzing data. It enables organizations to store and process data in a cost-effective manner while supporting a wide range of data analytics use cases.

Labels:
Location: San Francisco, CA, USA

Comments

Post a Comment

Popular posts from this blog

MySQL InnoDB cluster troubleshooting | commands

Cluster Validation: select * from performance_schema.replication_group_members; All members should be online. select instance_name, mysql_server_uuid, addresses from  mysql_innodb_cluster_metadata.instances; All instances should return same value for mysql_server_uuid SELECT @@GTID_EXECUTED; All nodes should return same value Frequently use commands: mysql> SET SQL_LOG_BIN = 0;  mysql> stop group_replication; mysql> set global super_read_only=0; mysql> drop database mysql_innodb_cluster_metadata; mysql> RESET MASTER; mysql> RESET SLAVE ALL; JS > var cluster = dba.getCluster() JS > var cluster = dba.getCluster("<Cluster_name>") JS > var cluster = dba.createCluster('name') JS > cluster.removeInstance('root@<IP_Address>:<Port_No>',{force: true}) JS > cluster.addInstance('root@<IP add>,:<port>') JS > cluster.addInstance('root@ <IP add>,:<port> ') JS > dba.getC...
Post a Comment
Read more

MySQL slave Error_code: 1032 | MySQL slave drift | HA_ERR_KEY_NOT_FOUND

MySQL slave Error_code: 1032 | MySQL slave drift: With several MySQL, instance with master slave replication, I have one analytics MySQL, environment which is larger in terabytes, compared to other MySQL instances in the environment. Other MySQL instances with terabytes of data are running fine master, slave replication. But this analytics environment get started generating slave Error_code :1032. mysql> show slave status; Near relay log: Error_code: 1032; Can't find record in '<table_name>', Error_code: 1032; handler error HA_ERR_KEY_NOT_FOUND; the event's master log <name>-bin.000047, end_log_pos 5255306 Near master section: Could not execute Update_rows event on table <db_name>.<table_name>; Can't find record in '<table_name>', Error_code: 1032; Can't find record in '<table_name>', Error_code: 1032; handler error HA_ERR_KEY_NOT_FOUND; the event's master log <name>-bin.000047, end_l...
2 comments
Read more

InnoDB cluster Remove Instance Force | Add InnoDB instance

InnoDB cluster environment UUID is different on node: To fix it stop group replication, remove instance (use force if require), add instance back Identify the node which is not in sync: Execute following SQL statement on each node and identify the node has different UUID on all nodes. mysql> select * from mysql_innodb_cluster_metadata.instances; Stop group replication: Stop group replication on the node which does not have same UUID on all nodes. mysql > stop GROUP_REPLICATION; Remove instances from cluster: Remove all secondary node from the cluster and add them back if require. $mysqlsh JS >\c root@<IP_Address>:<Port_No> JS > dba.getCluster().status() JS > dba.getCluster () <Cluster:cluster_name> JS > var cluster = dba.getCluster("cluster_name"); JS >  cluster.removeInstance('root@<IP_Address>:<Port_No>'); If you get "Cluster.removeInstance: Timeout reached waiting......" JS > cluster.removeInstance(...
2 comments
Read more