---
title: Delta Lake - An Efficient Solution to Bring Reliability to Data Lakes
description: Delta Lake an open-source data storage layer that delivers reliability to implements ACID transactions, scalable metadata handling, unifies the streaming.
image: https://www.xenonstack.com/hubfs/xenonstack-delta-lake-architecture.png
---

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# Delta Lake - An Efficient Solution to Bring Reliability to Data Lakes

[Chandan Gaur](https://www.xenonstack.com/blog/author/chandan-gaur) | 28 October 2022

## What is Delta Lake?

 Delta Lake is an open-source data storage layer that delivers reliability to data lakes. It 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](https://www.xenonstack.com/blog/apache-spark-architecture/)** APIs.

---

## Why Delta Lake?

 Are we making progress? Well, let's see what are the main benefits of implementing a Delta Lake in your company.

### **The Predicament with current Data Architectures**

 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](https://www.xenonstack.com/insights/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.

### **Delta Lake architecture, Lambda Architecture**

 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.

### **Hive on HDFS**

 Hive is formed on top of Hadoop. It is a data warehouse framework for implementing and examination of data that is collected in **[HDFS](https://www.xenonstack.com/blog/data-serialization-hadoop/)**. A hive is an open-source software that allows programmers to analyze massive data sets on Hadoop. In Hive, records and databases are built primary, and then data is arranged into these records. Hive is a data warehouse composed of handling and querying only structured information that is collected in logs. While dealing with structured data, Map Reduce does not have optimization and usability characteristics like UDFs, but the **[Hive](https://www.xenonstack.com/insights/apache-hive/)** framework does. Query optimization leads to an efficient method of query execution in terms of performance.

### **Hive on S3**

 Amazon Elastic MapReduce (EMR) provides a cluster-based distributed Hadoop framework that executes it transparent, quick, and cost-effective to process immense volumes of data over dynamically scalable Amazon EC2 instances. Apache Hive runs on Amazon EMR clusters and communicates with data collected in Amazon S3. A typical EMR cluster will have a master node, one or more core nodes, and arbitrary task nodes with a set of software resolutions that are proficient in shared parallel processing of data at scale. Delta Lake comprises features that combine data science, data engineering, and production workflows, perfect for the machine learning life cycle.

---

## How Delta Lake is different?

### **ACID transactions on Spark**

 Serializable isolation levels guarantee that users never see variable data. In a typical Data lake, several users would be accessing, i.e., Reading and writing the data in it, and the data integrity must be preserved. ACID is a critical feature in the bulk of the databases. Still, when it comes to HDFS or S3, generally, it is tough to give the same stability guarantees that ACID databases provide us. Delta Lake stores a transaction log to track all the commits done to the record directory to implement ACID transactions. Delta Lake architecture provides Serializable isolation levels to guarantee the data consistent crosswise many users.

### **Scalable metadata handling**

 Leverages Spark’s distributed processing power to manage all the metadata for petabyte-scale records with billions of files at ease.

### **Streaming and batch unification**

 In a Data lake, if we have a use state of both Stream processing and Batch processing, it is common to follow Lambda architecture. In Data lake, the data coming in as Stream (maybe from Kafka) or any past data you have (say HDFS) is the same record. It gives a consolidated glimpse of both these two distinct paradigms. A record in Delta Lake is a bunch record as well as a streaming origin and sink. Streaming **[Data Ingest](https://www.xenonstack.com/blog/big-data-ingestion/)**, batch historic backfill, interactive queries all just work out of the case.

### **Schema enforcement**

 Data Lake helps avoid detrimental data getting your data lakes by implementing the capability to define the schema and help execute it. It restricts data corruption by blocking the faulty data to get toward the system even before the data is ingested to the data lake by giving reasonable failure messages.

### **Time travel**

 Data versioning allows rollbacks, full past audit trails, and reproducible machine learning practices while using Delta Lake.

### **Upserts and deletes**

 Delta Lake architecture supports merge, update and delete operations to enable complex use cases like change-data-capture, slowly-changing-dimension (SCD) operations, streaming upserts, and so on.

![delta lake](https://www.xenonstack.com/hubfs/Imported_Blog_Media/cloud-security-failure-xenonstack-Jun-22-2021-02-14-58-19-PM-Jun-24-2021-04-52-28-90-PM-3-4.png)

 The solution to the "Data Lake dilemma" is to adopt Delta Lake. How?  **[Ask Our Big Data Consultant](https://www.xenonstack.com/services/big-data-services/)**

---

## Delta Lake Transaction Log

 Delta lake transaction logs, also known as delta log. It is an ordered record of all transactions that have been performed on the delta lake table since its creation. Use of transaction log: 1. To allow various users to read and write to the given table at the same time. It shows the correct view of data to users. It tracks all changes that users make to the table. 2. it implements atomicity on delta lake, it keeps watching on transactions performed on your delta lake either complete fully or not complete at all. Delta lake offers a guarantee of atomicity with the help of a transaction mechanism. It also works as a single source of truth

### Transaction Log Working and atomic commits

- Delta Lake automatically generates checkpoint files every ten commits. the checkpoint file saves the current state of the data in parquet format, which is easy for the spark to read quickly.
- Delta Lake is built on top of apache-spark, so multiple users cannot modify a table at once to handle this problem delta lake employs optimistic concurrency control when we need the same pieces of data at a time, that there’s a conflict and that’s optimistic concurrency control.

![transaction log working and atomic commits](https://www.xenonstack.com/hubfs/xenonstack-transaction-log-working-and-atomic-commits.png)

- Solving Conflicts Optimistically

![Solving conflicts automatically](https://www.xenonstack.com/hubfs/xenonstack-solving-conflicts-automatically.png)

---

## **Delta Lake Schema Enforcement**

 Schema enforcement also is known as schema validation. Delta Lake architecture ensures data quality by matching data to be written on the table. If the data’s schema is not matching with the table’s schema then it rejects the data. Like the front desk manager in some busy restaurant that only accepts reservations. Schema enforcement is a great tool. It works as a gatekeeper. It was enforced to those tables that directly feed to ML Algorithms, BI dashboards, **[Data Visualization Tools](https://www.xenonstack.com/blog/top-data-visualization-tools/)**, and a production system that required strongly typed and semantic data.

### Schema Evolution

 Schema evolution is a feature that allows users to change tables current Schema easily, mostly used when we are performing append or overwrite operations. It can be used at any time when you want to intend to change the table schema. After all, it is not hard to add a new column.

---

## Delta Time Travel for Data Lakes

 There is a new feature titled time travel capability. The next-generation unified analytics engine is made at the top of the apache-spark. Delta automatically versions all big data stored in Delta Lake. You can access any version of the data. Delta Lake architecture simplifies the **[Data pipeline](https://www.xenonstack.com/blog/data-pipeline/)** by making it easy to change and roll back data in any case of wicked writes. You can access versions of data in two different ways.

- **Using timestamp -** You can provide the timestamp or date string as an option to the DataFrame reader
- **Using a version number -** In Delta, every writer has a version number, and one can use it to travel back in time as well.

---

## Conclusion

 As the business issues and requirements grow over time, so does the structure of data. But, with the help of Delta Lake, incorporating new dimensions become easy as the data changes. Delta lakes are making data lakes' performance better, more reliable, and easy to manage. Therefore, accelerate the quality of the data lake with a safe and scalable cloud service.

- Get insight on **[Hadoop Delta Lake Migration](https://www.xenonstack.com/insights/hadoop-delta-lake-migration/)**
- Discover more about **[AWS Delta Lake](https://www.xenonstack.com/blog/aws-data-lake-analytics/)**

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## Agent SRE for Reliability and Observability Solutions

 AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability

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[Explore Agent SRE](https://agentsre.ai/)

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## Physical Surveillance with Vision AI Agent Technology

 AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly

- ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion
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## Agentic Data Intelligence Across Your Full Data Stack

 Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics

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## Intelligent Diagnostic for Self-Healing System Automation

 Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment

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## Agentic GRC - Monitoring Risk and Compliance Controls

 AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready

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✕

## Agentic Finance and Procurement Intelligent Agents

 Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter

- ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments
- ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance
- ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions

[Optimize Finance & Procurement](https://www.xenonify.ai/)

![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg)

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  },
  "dateModified" : "2022-10-28T19:46:57.565Z",
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    },
    "name" : "Xenonstack Inc"
  }
}
```

```json
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  "@type" : "Organization",
  "address" : {
    "@type" : "PostalAddress",
    "addressCountry" : "USA",
    "addressLocality" : "Plano",
    "addressRegion" : "Texas",
    "postalCode" : "75024",
    "streetAddress" : "7700 Windrose Ave. "
  },
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  },
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  "telephone" : "",
  "url" : "https://www.xenonstack.com/"
}
```

```json
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}
```