E-MapReduce:DeltaLake

Traditional data lake solutions allow you to use a big data storage engine, such as Alibaba Cloud Object Storage Service (OSS) or Hadoop Distributed File System (HDFS), to build a data lake and store various types of data in the data lake. You can connect the data lake to an analytics engine such as Spark or Presto to analyze the data stored in the data lake. Traditional data lake solutions have the following disadvantages:

• Data ingestion may fail. If data ingestion fails, dirty data cleanup and job recovery are required.

• Data quality control is unavailable due to the lack of extract, transform, load (ETL) operations.

• In streaming processing and batch processing, no transactions can be used to isolate reads and writes.

Description of the Delta Lake solution:

• This solution provides a data management layer on top of the big data storage layer. This data management layer functions in a similar manner to the metadata management module for a database. Delta Lake metadata is stored together with data and is visible to users. Data warehouses and data lakes shows an example.

• Delta Lake provides atomicity, consistency, isolation, durability (ACID) transactions based on metadata management to prevent malformed data ingestion and isolate reads and writes during data ingestion. This also ensures that dirty data is not generated.

• Field information is stored in metadata. Delta Lake verifies data during data ingestion to ensure data quality.

• The transaction feature isolates reads from writes in streaming processing and batch processing.

The following table compares a data warehouse, a traditional data lake, and Delta Lake.

Delta Lake is an ideal solution to manage cloud-based data lakes. You can use Delta Lake in the following scenarios:

• Real-time query: Upstream data is ingested to Delta Lake in real time to support real-time query. For example, in Change Data Capture (CDC) scenarios, when you use a Spark Streaming job to consume binary logs in real time, you can use the merge feature that is provided by Delta Lake to merge upstream data updates to Delta Lake in real time. This way, you can use Hive, Spark, or Presto to query the data in real time. ACID transactions isolate data writes from queries to avoid dirty reads.

• Data deletes and updates due to General Data Protection Regulation (GDPR) requests: Traditional data lakes do not support data deletes or updates. If you want to delete data, you must manually delete the data. If you want to update data, you must manually delete the original data and write the updated data into the storage. Delta Lake supports data deletes and updates.

• Real-time data synchronization based on CDC: You can run a streaming job to merge upstream data updates to Delta Lake in real time by using the merge feature that is provided by Delta Lake.

• Data quality control: Delta Lake provides the schema verification feature that allows you to remove abnormal data during data ingestion. You can also use this feature to process abnormal data.

• Data evolution: You may need to change your data schema. Delta Lake provides an API for you to change your data schema.

• Real-time machine learning: In machine learning scenarios, you may require a significant amount of time to process data, such as to cleanse, convert, and characterize data. You also need to separately process historical data and real-time data. Delta Lake simplifies these workflows. Delta Lake provides a complete and reliable real-time stream to cleanse, convert, and characterize data. You do not need to separately process historical data and real-time data.