What Is Lambda Architecture?

Data architectures play an important role in processing data that’s too large for your typical database system. With advancements in data capabilities over the years, particularly with big data enabling artificial intelligence, the demand for robust big data architectures has increased significantly. These architectures help in several stages, from the ingestion and processing of data to its analysis. One solution in this space is lambda architecture. Lambda architecture allows you to increase the speed at which systems can handle large data sets by enabling data to flow through two paths. 

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What is lambda architecture?

Lambda architecture is a data processing model that provides two data pipelines. These two data pipelines mean that data processing can occur in real-time. Consequently, lambda architecture allows you to handle significant amounts of data for faster implementation. In addition to the two high-speed data pipelines, lambda architecture contains a serving layer. Thanks to the real-time processing lambda architecture support, the serving layer allows you to query efficiently and perform queries that include recent data.

Read more: What Is a Data Model?

Real-world applications of lambda architecture

Lambda architecture has a variety of use cases in big data environments. Here’s a look at several of these real-world applications:

• Social media analytics: Lambda architecture in social media analytics allows you to perform sentiment analysis. Sentiment analysis allows businesses to understand how people talk about them on social media to gauge public opinions and overall feelings toward the company.

• Fraud detection: Lambda architecture can help with real-time fraud detection. It can directly compare historical data with real-time data to identify concerning activity.

• Marketing: Personalized marketing approaches allow you to make specific recommendations for different customers. By analyzing historical and real-time data with lambda architecture, you can take a data-driven approach to building better relationships with consumers and offering products they’re more likely to be interested in.

Who uses lambda architecture?

Professionals in data science and other positions that handle immense amounts of data benefit from using lambda architecture, ensuring data is always rapidly available and up to date. Here are some of the different positions you can find that use big data and data science:

• Data architect

• Database developer

• Database administrator

• Data scientist

• Big data engineer

• Data analyst 

• Quantitative analyst

• Data engineer

• Machine learning engineer

• Business intelligence analyst

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Lambda architecture components

Lambda architecture has several layers that work together. Here’s a look at the components that allow you to process data.

1. Batch layer

The batch layer receives data simultaneously with the speed layer. This layer prepares data for indexing and stores it within a data warehouse. The batch layer operates regularly, having a consistent routine that you can predetermine. You can use tools such as Apache Hadoop to help support this process.

2. Serving layer

The serving layer is where querying occurs. Since the data processing runs through parallel pipelines, you can index data quickly. Receiving outputs from batch and speed layers allows you to perform real-time querying in the serving layer.

3. Speed layer

The speed layer helps index recent data that the serving layer has yet to complete indexing. Since the batch layer lacks the same high-speed capabilities as the speed layer, it helps increase the rate at which data becomes available for querying. The speed layer deletes any data that’s not needed following the indexing.

How lambda architecture works

Lambda architecture works by creating two distinct paths for data processing. These two paths are the batch layer and the speed layer. Historical data processing occurs in the batch layer, while the speed layer is in charge of processing all the real-time data and data that are close to real-time.

This system works well because combining the historical data from the batch layer and more immediate data from the speed layer allows you to develop analytical insights and queries from a more holistic point of view. Rather than being driven by solely historical or real-time data, you get the benefits of both.

Pros and cons of lambda architecture

Lambda architecture offers several benefits. However, it also presents challenges.

Pros

• Lambda architecture simplifies scaling, allowing you to scale applications to meet your specific demands automatically. 

• Due to its sequential nature, Lambda architecture provides highly consistent data, allowing the speed and batch layers access to recent data. 

• Lambda architecture is a serverless system, which helps reduce the risk of errors due to a system crash and does not require server maintenance or updates. 

Cons

• Due to the significant complexity of lambda architecture, maintaining these systems can be challenging in terms of coding and debugging.

• Running the batch and speed layers simultaneously places significant demands on the computing system, making the processing less efficient. 

Lambda architecture vs. kappa architecture

One alternative to lambda architecture is kappa architecture. The main difference between these two systems is that data flows through just one pipeline in kappa architecture rather than the two paths in lambda architecture. This design aims to provide an alternative with less complexity than that of lambda architecture. 

Using kappa architecture can reduce costs and improve operational efficiency. However, lambda architecture is great at constantly updating data, making it a good fit for certain applications, such as machine learning.

How to get started in lambda architecture

To get started in a career working with big data architecture, such as lambda, you can earn a bachelor’s degree in a relevant area, such as computer science, data science, information technology, or computer engineering. You can further advance your data architecture skills by earning relevant certifications, such as Google Cloud’s Professional Data Engineer certification or an IBM Certified Solution Architect certification.

Getting started with Coursera

On Coursera, you can find highly rated courses on how to learn more about working with big data and data analytics. Modern Big Data Analysis with SQL from Cloudera can help you learn the basics of SQL to perform queries.

You can also earn an IBM Data Analyst Professional Certificate. In this course, you will practice SQL, Python, and several other valuable data analysis skills while earning an employer-recognized certificate. Upon completing either program, gain a shareable certificate to include in your resume, CV, or LinkedIn profile.

specialization

Modern Big Data Analysis with SQL

Learn Data Analysis for Big Data. Master using SQL for data analysis on distributed big data systems

4.8

(1,179 ratings)

29,253 already enrolled

Beginner level

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Average time: 1 month(s)

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Skills you'll build:

Database Management, Data Management, Operational Databases, Databases, Apache Hive, Apache Hadoop, Performance Tuning, SQL, Amazon S3, Relational Databases, Data Storage, Data Warehousing, Database Systems, MySQL, Database Design, Big Data, PostgreSQL, File Systems, Data Analysis, Cloud Storage, Virtualization and Virtual Machines, Virtual Machines, Data Manipulation, Command-Line Interface

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Excel Formulas, SQL, Dashboard, Exploratory Data Analysis, Python Programming, Microsoft Excel, Big Data, Data Wrangling, Matplotlib, Data Visualization, Predictive Modeling, Interactive Data Visualization, IBM Cognos Analytics, Data Import/Export, Data Storytelling, Professional Networking, Data Visualization Software, Generative AI, Data Analysis, Plotly, Interviewing Skills, LinkedIn, Analytical Skills, Professional Development, Business Writing, Presentations, Relationship Building, Recruitment, Portfolio Management, Pandas (Python Package), Jupyter, Data Manipulation, Relational Databases, Databases, Query Languages, Transaction Processing, Stored Procedure, Regression Analysis, Scikit Learn (Machine Learning Library), NumPy, Data Cleansing, Descriptive Statistics, Data Pipelines, Feature Engineering, Supervised Learning, Statistical Modeling, Data-Driven Decision-Making, Data Collection, Data Lakes, Apache Spark, Statistical Analysis, Apache Hadoop, Data Warehousing, Analytics, Data Mart, Data Science, Apache Hive, Histogram, Looker (Software), Web Scraping, Scatter Plots, Box Plots, Data Transformation, Data Quality, Pivot Tables And Charts, Google Sheets, Spreadsheet Software, Data Integrity, Information Privacy, Tree Maps, Prompt Engineering, Responsible AI, Data Ethics, OpenAI, Seaborn, Heat Maps, Geospatial Information and Technology, Data Presentation, Data Structures, Object Oriented Programming (OOP), JSON, Restful API, Scripting, Automation, Application Programming Interface (API), Programming Principles, Computer Programming, Data Processing