Delta Lake is an open format storage layer that delivers reliability, security and performance on your data lake — for both streaming and batch operations. By replacing data silos with a single home for structured, semi-structured and unstructured data, Delta Lake is the foundation of a cost-effective, highly scalable lakehouse.
Delta Lake is an open-source storage layer that delivers reliability to data lakes. Delta Lake implements ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. Delta Lake architecture runs on top of your current data lake and is fully cooperative with Apache Spark APIs.
Are we making progress? Well, let’s see what are the main benefits of implementing a Delta Lake in your company.
Current big data architectures are challenging to develop, manage, and maintain. Most contemporary data architectures use a mix of at least three varying types of systems: streaming systems, data lakes, and data warehouses. Business data comes through streaming networks such as Amazon Kinesis or Apache Kafka, which mainly focus on accelerated delivery. Then, data is collected in data lakes, such as Apache Hadoop or Amazon S3, which are optimized for large-scale, ultra-low-cost storage. Lamentably, data lakes individually do not have the performance and quality required to support high-end business applications: thus, the most critical data is uploaded to data warehouses, which are optimized for significant performance, concurrency, and security at a much higher storage cost than data lakes.
Lambda architecture is a traditional technique where a batch system and streaming system prepare records in correspondence. The results are then merged during query time to provide an entire answer. Strict latency requirements to process old and newly formed events made this architecture famous. The key downside to this architecture is the development and operational overhead of maintaining two different systems. There have been efforts to ally batch and streaming into a single system in history. Companies have not been that victorious though in those attempts. With the arrival of Delta Lake, we are seeing a lot of our clients adopting a simple constant data flow model to process data as it comes. We call this architecture, The Delta Lake architecture. We cover the essential bottlenecks for using a continuous data flow model and how the Delta architecture resolves those difficulties.
#big data engineering #blogs #delta lake #delta lake architecture #delta lake spark
Enterprise has been spending millions of dollars getting data into data lakes using Apache Spark with the aspiration to perform Machine Learning and to build Recommendation engines, Fraud Detection, IoT & Predictive maintenance etc. But the fact is majority of these projects are failing in getting the reliable data.
“Delta Lake overcomes the above challenges”
Databricks open sourced their proprietary storage name in the name of Delta Lake, to bring ACID transactions to Apache Spark and big data workloads. Earlier Delta lake is available in Azure/AWS Databricks only where the data will get stored only on DBFS, which may lie on top of ADLS/S3. Now Delta format can lie on HDFS, ADLS, S3 or local File system, etc…. Delta Lake is also compatible with MLFlow.
How Delta Works?
Delta lake is based on Parquet, it adds the transactional awareness to Parquet using transaction log which will be maintained in additional folder (_delta_log ) under the table directory. Lot of vendors like Informatica, Talend embrace delta and working on native readers and writers.
Json file under the _delta_log folder will have the information like add/remove parquet files(for Atomicity), stats(for optimized performance & data skipping), partitionBy(for partition pruning), readVersions(for time travel), commitInfo(for audit).
Below is the Json file which present in delta transactional log when we write sample DataFrame with 2 records. Notice that it analyse the status like min,max in each file which helps to effectively skips the unnecessary data and helps in performance optimization.
#spark #databricks #delta #delta-lake #big-data #data analysis
Data Lake is a storage repository that cheaply stores vast raw data in its native format.
It consists of current and historical data dumps in various formats, including XML, JSON, CSV, Parquet, etc.
Delta Lake allows us to incrementally improve the quality until it is ready for consumption. Data flows like water in Delta Lake from one stage to another stage (Bronze -> Silver -> Gold).
Data may come from various sources, which could be Dirty. Thus, It is a dumping ground for raw data.
Consists of Intermediate data with some cleanup applied.
It is Queryable for easy debugging.
It consists of clean data, which is ready for consumption.
Original article source at: https://www.c-sharpcorner.com/
Data Lake 是一个存储库，可以廉价地以其本机格式存储大量原始数据。
它由各种格式的当前和历史数据转储组成，包括 XML、JSON、CSV、Parquet 等。
Delta Lake 使我们能够逐步提高质量，直到可以使用为止。数据像 Delta Lake 中的水一样从一个阶段流到另一个阶段（青铜 -> 白银 -> 黄金）。
Озеро данных — это репозиторий, в котором дешево хранятся огромные объемы необработанных данных в собственном формате.
Он состоит из дампов текущих и исторических данных в различных форматах, включая XML, JSON, CSV, Parquet и т. д.
Delta Lake позволяет нам постепенно улучшать качество, пока оно не будет готово к употреблению. Данные перетекают, как вода в озере Дельта, от одного этапа к другому (бронза -> серебро -> золото).
Данные могут поступать из различных источников, которые могут быть грязными. Таким образом, это свалка для необработанных данных.
Состоит из промежуточных данных с некоторой очисткой.
Это Queryable для легкой отладки.
Он состоит из чистых данных, готовых к использованию.
Оригинальный источник статьи: https://www.c-sharpcorner.com/