Serverless Solutions for Big Data and Data Lake

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by Navdeep Singh Gill | July 22, 2018  Serverless Solutions for Big Data and Data Lake

Why Serverless?

Cloud Computing enabled the self-service provisioning and management of Servers. However, as we know in the world of Big Data, Dynamic Scaling and Cost Management are the keys factors behind the success of any Analytics Platform.

So, The Server Architecture exactly does that. Many Cloud Platforms and Open Source Technologies has launched many services which are serverless in which code execution will scale up or down as per the requirement, and we have to pay for Infra only for the execution time of our code.

Its main advantage is that Developer does not have to think about servers ( or where my code will run) and he needs to focus on his code.

All Infra Design handled by some third party services where the code runs on their containers using Functions as a Service, and they further communicate with the Backend as a service for their Data Storage needs.

Examples:

  • AWS
  • Azure
  • Google
  • OpenFaas ( with Kubernetes)

Serverless for Big Data

Serverless is becoming very popular in the world of Big Data. As our Big Data Workloads are managed by Serverless Platforms so We don’t need an extra team to manage our Hadoop/Spark Clusters.

Let’s see various points which we can consider while setting our Big Data based Platforms.

No need of Infra Management

While working on various ETL and Analytical platforms, We found that we need many guys who can set up the Spark, Hadoop clusters and nowadays, We use Kube Cluster and everything launched on containers. So, Monitoring them and Scaling the resources, cost optimization takes a lot of effort and resources. So Serverless make developer and manager’s life easy as they don’t have to worry about the infra.

Scale on Demand

Serverless Platforms continuously monitor the resource usage of our deployed code ( or functions) and scale up/down as per the usage. So, Developer doesn’t need to worry about the scalability.

Just Imagine, We have deployed some ETL job on Spark Cluster, and it runs after every hour and let’s say at peak times, many records to extract from Data Source per hour increases to 1 million and sometimes, in midnight, it falls to the only 1k to 10k.Serverless ETL Service automatically scales up/down our job according to requirement. It's like same we do in our Kubernetes cluster using AutoScale Mode, in that we just set the rules for CPU or Memory Usage and Kubernetes automatically takes care of scaling the cluster.

Cost Effective

Cost Effective means that we have to pay only for the execution time of our code. It means when our deployed function is idle and not being used by any client, we do not have to pay for any infra cost for that. It's like we do not have to pay on an hourly basis to any Cloud Platform for our Infra. Its like they launch the things on the fly for us.

Example: Serverless ETL platform like Glue launches the Spark Jobs according to the scheduled time of our ETL Job. So, Cloud Service will charge us only for that particular time of execution.Also, Imagine you have several endpoints/microservice / API which less frequently used. So for that type of cases, Serverless architecture is best as we will be charged only whenever those API’s will be getting called.

Built-In High Availability and Fault Tolerance

The primary Serverless Architecture Providers provides built-in High Availability means our deployed application will never be down.

It’s same like we use Nginx for any application and having multiple servers deployed and Nginx automatically takes care of routing our request to any available server.

In the context of Big Data, Let’s say Our Spark’s ETL Job is running and suddenly Spark Cluster gets failed due to many reasons. So Glue will automatically re-deploy our Spark Job on the new cluster, and Ideally, Whenever a job fails, Glue should store the checkpoint of our job and resume it from wherever it fails.

Serverless Real-Time Analytics

Here we will discuss that how we can set up real-time analytics platform using Serverless Architecture.

Real-time Analytics Workflow:

Analytics Workflow

Data Collection Layer 

In Real-time Analytical Platforms, Data Sources like Twitter Streaming, IoT Analytics, etc push data continuously, So the First task in these platforms is to build a unified Data Collection Layer where we can define all these Data Sources and write it to our Real-time Data Stream which can be further processed by Data Processing Engines.

We can use AWS Cloud DataFlow for AWS Platforms, Google Cloud DataFlow for Google Platforms, Azure DataFactory for Azure Platforms and Apache Nifi in case of open source platforms for defining Streaming Sources like Twitter Streaming or other Social Media Streaming which continuously loading data from Twitter Streaming EndPoints and writing it to our Real-time Streams.

Data Streaming Layer 

Now, we do not know that how much producers can write data means We cannot expect a fixed velocity of incoming data. So We need real-time storage which can scale up in case of a massive increase of incoming data and also scales down if the incoming data rate is slow.

For Serverless Real-time Stream,

  • AWS provides Kinesis Streams and DynamoDB Streams
  • Google provides PUB/SUB service
  • Azure provides Event Hubs, IoT Hubs

Data Processing Layer 

The layer where we often do some Data preprocessing like Data Cleaning, Data Validation, Data Transformations, etc.

In this layer, We also perform real-time analytics on incoming streaming data by using the window of last 5 or 10 minutes, etc.

So While doing this stuff on Real-time Stream, We need a Data Processing Platform which can process any amount of data with consistent throughput and writes data to Data Serving Layer.

Example:

In AWS Platforms, We can configure our DynamoDB Streams with AWS Lambda Function which means whenever any new record gets entered into DynamoDB, it will trigger an event to our AWS Lambda function, and Lambda function will do the processing part and write the results to another Stream, etc.

In Google Platforms, We can do it using Google PUB/SUB and Google Cloud Functions/Spark using Data Proc.

In Azure, We can use Azure EventHub and Azure Serverless Function for the same.

Data Serving Layer 

This layer is responsible for serving the results produced by our Data Processing Layer to the end users. So This layer should also be dynamically scalable because they have to serve millions of users for Real-time Visualization.

So There are two types of Serving Layer :

Streams: In AWS, We can choose DynamoDB Streams as our Serving Layer on which Data Processing layer will write results, and further a WebSocket Server will keep on consuming the results from DynamoDB and WebSocket based Dashboard Clients will visualize the data in real-time. While Google PUB/SUB and Azure EventHub can be also used as a Streaming Serving Layer.

NoSQL Datastore: We can use DynamoDB NoSQL Datastore as our Serving layer as well on top of which we can build a REST API, and Dashboard will use REST API to visualize the real-time results. Azure Cosmos DB and Google Cloud Datastore can also be used for the same.

Serverless Batch Processing

In this part, we will see how we can do batch processing using serverless architecture. We can have various use cases where we need Batch Processing of Data.

  • Define an ETL Job in which Data needs to be pulled from any OLTP Database like Amazon RDS or any other database, run transformations and needs to be stored into our Data Lake ( like S3, Google Cloud Storage, Azure Storage ) or Data Warehouse directly ( like Redshift, BigTable or Azure SQL Data Warehouse ).
  • Define an ETL Job in which Data needs to be pulled from Data Lake and need to run transformations and move the data to Data Warehouse.

And many more use cases as well. All of these use cases are related to Batch Data Processing. In Batch Data Processing, we have to pull data in increments from our Data Sources like fetching new data from RDBMS once a day or pulling data from Data Lake every hour.

So The Challenge in Batch Job Processing is that we don’t know how much data we are going to have in next increment. So Our Batch Data Processing Platform should be scaled automatically, and also Serverless architecture will also be cost efficient because as we know that Batch Jobs will run hourly or daily etc. Then we don’t need to launch a Hadoop or Spark Cluster for that.

Serverless ETL Platform like Glue which will charge us only when our ETL Job will run and also scale automatically according to resources required for ETL job.

Serverless Databases

The concept of Serverless Architecture is also becoming popular in databases also. Amazon has launched its Aurora Serverless Database which redefines the way we use our databases. We already used a lot of ways to optimize the read/write capabilities of database like using Cache frequent queries to optimize the reads, using compression techniques to optimize the storage etc.

Serverless Databases

But Serverless Architecture focuses on decoupling the Compute Nodes and Storage Nodes. And Not only Decoupling, It should be managed automatically means auto-startup/shutting down of database servers, scaling up / down according to the workload on database servers.

Also, Costing should also be based on usage like Amazon Aurora do it on a per-second basis. So It means you don’t have to pay for database server infra all the time. You have to pay only for the time when database was in active state.

Serverless Data Lake Architecture

Data Lake refers to storage where we have data in its natural state.

Process involved in building Data Lake:

Data Ingestion

Able to ingest any data from different types of Data Sources ( Batch and Streaming ) and should be scalable to handle any amount of data and costing should only be for the execution time of Data Migration Jobs.

Example: AWS Glue for Batch Sources and Kinesis Firehose & Kinesis Streams with AWS Lambda for Streaming Sources.

Data Storage

Should be scalable to store multi years data at low cost and also file type constraint should not be there.

Example: AWS S3, Google Cloud Storage, Azure Storage

Data Catalogue

Catalogue Service which should be updated continuously as we receive data in our Data Lake. We can enable Data Discovery only if we have Data Catalogue which keeps updated metadata about the Data Lake.

Example: AWS Glue Data Catalogue Service , Apache Atlas , Azure Data Catalog

Data Discovery/Searching

Serverless Querying Engine for exploring the Data Lake and it should also be scalable up to thousands & more queries and charge only when query is executed.

While Migrating data from our operational systems to Data Lake/ Warehouse,There are two types of approaches.

  • ETL (Extract, Transform and Load )

  • ELT (Extract, Load, and Transform )

ETL vs ELT

In ETL Approach, Generally Data is extracted from the Data Source using Data Processing Platform like Spark and then data is transformed and Then it loaded into Data Warehouse. But in ELT Approach, Data is extracted and directly loaded into Data Lake, and Then Data Transformations Jobs are defined and transformed data gets loaded into Data Warehouse.

As we know that in the world of Big Data, there are different types of Data Sources like REST API, Databases, File Systems, Data Streams etc and they have different varieties of Data like JSON, Avro, Binary Files ( EBCDIC), Parquet etc.So There can be use cases, in which we just want to load data as it is into our Data Lake because we can define transformations on some data after exploration only.

So, That’s Why ELT approach is better than ETL approach in which Data is loaded as it is into Data Lake and Then Data Scientists use various Data Wrangling tools to explore and wrangle the data and Then define the transformations and then it got committed/loaded into Data Warehouse.

Building a Serverless Analytics Solution

As we have explained How to build a Data Lake using Server Architecture, Now Let’s see how we can build Big Data Analytics Solution using Serverless Architecture. Big Data Analytics can be used for various purposes :

  • Data Scientists need to explore the data in Data Lake.
  • Business Team needs to analyze their business in various prospects from Data Lake.
  • Machine Learning and Deep Learning Models are also got offline trained by reading new data from Data Lake periodically.

So There are few key points which needs to be considered while building Serverless Analytics Solution:

  • Should have a Data Discovery Service which should charge us only for the execution time of queries.
  • Should be scalable for unlimited queries over Data Lake so that Concurrently multiple users can discover the Data Lake simultaneously.

Serverless Analytics Example

Now Let’s say we have a Data Lake on our Cold Storage like S3 or HDFS or Glusterfs using AWS Glue or any other Data Ingestion Platform.

Now We want to run SQL query on any amount of data, and there can be multiple users who can run complex analytical queries on the data.

We need a query engine which can run multiple queries with consistent performance. Just Imagine, We have a spark cluster deployed with some 100 Gigs of RAM, and we are using Spark Thrift Server to query over the data, and we have integrated this thrift server with our REST API, and our BI(Business Intelligence) team is using that dashboard.

Spark Cluster able to run the analytical queries correctly with only a few queries hit by BI team, If no of concurrent users reached to 50 to 100, then the queries are waiting for the stage, and they will be waiting for earlier queries to get finished and free the resources and then those queries will start executing.

So, Here is the point, We need a Serverless Query Engine which can serve as many users as per requirement without any degradation in performance.

We can build this type of Interactive Queries Platform using AWS Serverless Services like Amazon S3, Athena, and QuickSight.

Amazon S3 offers unlimited space, and Athena offers serverless querying engine, and QuickSight allows us to serve concurrent users.

For Reporting Services, We can use Amazon Athena too by scheduling them on AWS Cloud Dataflow. In Google Platforms, We can use Google BigQuery as Querying Service.

Serverless Microservices

The Microservices architecture allows our application to divide into logical parts which can be maintained independently. It eases and fastens the process of continuous deployment and automation testing. It also enables cross-language communication like Data Scientist uses R Language for his ML/DL Model Development and if he wants to access data, then he just needs to use another microservice using API Gateway which can be developed in Scala, Python etc.

Now Let’s see What Serverless MicroServices offers us:

Cost Optimization

You will be charged only for the execution time of microservice which is used by any type of client.

Auto Scaling

Our Microservice will be automatically scaled according to its workload, So No need of DevOps Team for monitoring the Resources.

Focus Only on Code

Developer can just focus only on his code and no need to worry about deployment part and other things. Earlier, When developer is working on the code, then he has to take Load Factor into consideration as well due to deployments on servers. But now if your code is written properly which can handle computations in a parallel way, then rest of the things will be handled by Serverless Functions easily as they will scale automatically.

Composition

Let’s say we have use case in which there is a microservice that is collecting stocks data from third-party API and saving it to our Data Lake and Let’s say Then it triggers a Kafka Event and There is another Spark Streaming MicroService which is continuously reading the Kafka events and will read the file from Cloud Storage and do transformations and persist the data to warehouse and trigger the Current Stocks microservice to update the latest stocks information of various companies.

So This communication among MicroServices is called Composition. Serverless Architecture simplifies the lifecycle of these types of microservice patterns by managing them independently.

Flexibility of using various Cloud Platform

Various Cloud providers support Serverless Platforms like AWS Lambda, Google Cloud Function, Azure Functions etc. So Developers have the flexibility of deploying their serverless function on different Cloud Platforms.

Know When to Implement Serverless vs. Containers

As we know that Kubernetes are very popular nowadays as they provide Container based Architecture for your Applications. But the questions how we are going to take decision over our Application Deployment on Serverless vs Container.

Here are some points which are lacking in Serverless Platforms as compared to Containers:

Stateless vs Stateful Apps

So Serverless Application is like decoupling all of the services which should run independently. So, Serverless Application works best when we are following Stateless Architecture in which One microservice doesn’t depend upon the state of other microservice. However, in container-based applications, we can attach Persistence Storage with containers for the same. So, It's better to use both container and serverless architecture together and deploy only those applications on serverless which are independent and needs to be accessed directly from outside. Otherwise, Go for Container-based architecture.

Memory and Concurrency

We talked about auto-scaling of Resources like CPU and Memory in Serverless Computing like AWS Lambda, but AWS Lambda has some restrictions also on it. Maximum Memory we can allocate to our AWS Lambda Function is 1536 MB, and concurrency also varies according to your AWS region, it changes from 500 to 3000 requests per minute.But in the world of Containers, There are no such restrictions. We can enable the auto-scaling in Kubernetes and scale up/down our application according to any workload.

Language Support

There is also a restriction of language support in Serverless Platforms like AWS Lambda. It only supports Node.js, Python, Java, Go, C#. Scala and other Languages are not supported yet. So REST API developed in Scala using Akka and Play Framework are not yet supported on AWS Lambda.

Control Level and Security

We have full control over our Infra, and we can allocate resources according to our workload. We can set fine-grained rules and policies on our application access. But in Serverless, You have to trust on Serverless Platforms for this. So, If security is a major concern for you and you want it very customized, then Containers are a good fit.

Debugging, Testing and Monitoring Flexibility

Serverless Compute offers Monitoring by cloud watch, and you can monitor some parameters like concurrent connections and memory usage etc. But still, Deep level of monitoring is not there like Average time taken by request, and other performance metrics can’t be traced, and also We can’t do deep Debugging also in Cloud-based Serverless Functions. So, For those Applications, which needs high performance then we have to think about our performance expectations before we use Serverless Platforms.

Cost Effective

Containers are always in active mode with a minimum number of resources which are required for an application, and you have to pay for that infra. But in case of Serverless, In case of no usage, our container can completely shut down, and you have to pay only for the execution time of your Function. Serverless Container is often used cold start because container got shut down in case of no usage. So it can take time to serve in that scenario.

Use Cases of Serverless Architecture and Design Patterns

Now we will be discussing few use cases of serverless architecture which are handled more efficiently by Serverless Architectures.

Event-driven data processing

We were working on decoding the EBCDIC files which were gets stored on our S3 Buckets by an external application. So What we do earlier is deploy a Spark Job on our EMR Cluster which was listening to AWS SNS Notification Service and use the Cobol layout to decode the EBCDIC to parquet format and perform some transformations and move it to our HDFS Storage.

So We were always paying for EMR Cluster on per hour basis. So We use the same conversion and transformation logic in our AWS Lambda function and What it does is save our infra cost, and we have to pay whenever we got any new EBCDIC file in our S3 Buckets.

Serverless Web applications

Let’s say we have a Web Application hosted on our On-Premises or Cloud Instance like EC2. Now we have to pay for the infra always on which REST API deployed. So, We can deploy our API as AWS Lambda functions, and we will be charged only whenever any traffic incur or whenever that specific API called, and another benefit is we don’t have to worry about the scalability as AWS Lambda automatically scale up or down our API’s according to load on it.

Mobile and Internet-of-Things applications

While working on various cases of IoT Analytics Platform, we choose AWS Lambda as our Serverless Data Processing and Transformation Service in which AWS Lambda is continuously consuming data from Kinesis Streams and perform the Data Cleaning, Transformations and Enrichment on the data and store it to Redshift and DynamoDB.

Real-time Log Monitoring & Alerting

Using CloudTrail and CloudWatch, we enabled real-time log monitoring using AWS Lambda functions in which we keep on consuming the log events using Cloud Watch which are generated by CloudTrail. Then, After doing some parsing of logs, we are monitoring the metrics and check for any critical event and generate alerts to our notification platforms like Slack, RocketChat, Email, etc. immediately in our AWS Lambda Function.

Schedule our Everyday Tasks like Database BackUp

We use a combination of Amazon SNS Service and AWS Lambda Function to automate our Database Backup Jobs. Now, the plus point is we have to pay for only that time whenever our database backup job initiated.

Serverless Architectures On Different Cloud and On-Premises

There are also various platforms in the market which are providing Serverless Services for various components of our Big Data Analytics Stack.

AWS Big Data Serverless

Amazon S3

Cloud Scale Storage is the critical point for the success of any Big Data Platform. Amazon S3 is warm storage, and it is very cheap, and We don’t have to worry about its scalability of size. So We only have to pay for what we store in it, and we don’t need to worry about the cost of infra where we need to deploy our storage. We often use Amazon S3 as Data Lake, and Batch Queries in our Analytics Platform we can run Ad hoc Analytical queries using Spark or Presto over it. Also, We define our transformations jobs in Spark which checks for new data in S3 Buckets periodically and transform it and store it to our Data Warehouse.

Amazon Glacier

Amazon Glacier is also cheaper storage than Amazon S3, and we used it for achieving our data which needs to be accessed less frequently. So Batch Queries which needs to be run weekly or monthly, we use Amazon Glacier for that.

Amazon DynamoDB

Amazon DynamoDB is powerful NoSQL Datastore which built upon Serverless Architecture, and it provides consistent single-digit millisecond latency at scale. Here also, pay for whenever you perform any read/write request. We use Amazon DynamoDB as Serving Layer for Web and Mobile Apps which needs consistent read and write speed.

AWS Lambda

AWS Lambda is compelling service launched by AWS and based upon Serverless Architecture where we can deploy our code, and AWS Lambda functions and Backend Services manage it.

It scales up/down according to incoming rate of events, and it can trigger from any Web or Mobile App. So it provides seamless integrations with almost every type of client. Moreover, We will charge per 100ms of our execution time.

We deploy our REST API’s on AWS Lambda using its support for Spring Framework in Java, and It also supports Node js, Python, and C# language too.

Another use case we mostly use this AWS Lambda is for Notification Service for our Real-time Log Monitoring. We ingest real-time logs from Kafka Streams and process it in Lambda Functions and generate alerts to Slack, Rocket-Chat, email, etc. if any critical situation detected from logs.

Interactive Query Service

Amazon Athena is very power querying service launched by AWS, and we can directly query our S3 data using Standard SQL. Moreover, yes, it is serverless as It can scales up/down as our query requirement, and We have to pay per query.Amazon Athena also supports various format also like Parquet, Avro, JSON, CSV, etc.

AWS Glue

AWS Glue is serverless ETL Service launched by AWS recently, and it is under preview mode and Glue internally Spark as execution Engine. Moreover, Glue is capable of handling the massive amount of data, and we can transform it seamlessly and define the targets like S3, redshift, etc. Glue also allows us to get the ETL script in python or scala language and We can add our transformation logic in it. Then Upload it back to Glue and then just let Glue do the things for you.

Google Big Data Serverless

Cloud Function

Google Cloud Service in which we can define our business logic to ingest data from any data source like Cloud Pub/Sub and perform Data Transformations on the fly and persist it Into our Data Warehouse like Google Big Query or again to Real-time Data Streams like Google PUB/SUB.

Cloud Datastore

NoSQL Service provided by Google Cloud, and it follows serverless architecture and its similar to AWS DynamoDB.

Cloud Storage

Object Storage service like AWS S3 which is highly scalable and cost-effective.

Cloud DataFlow

Serverless Stream and Batch Data processing Service provided by Google Cloud in which we can define our Data Ingestion, Processing & Storage Logic using Beam API's and deploy it on Google Cloud Dataflow.

Google BigQuery

Google BigQuery is serverless data warehouse service, and Google Cloud Services fully manage it.

Cloud ML Engine

Google Cloud also has a Cloud ML Engine to provide serverless machine learning services which scales automatically as per Google hardware, i.e., Tensor Processing Units.

Oracle Serverless

Oracle has also launched an Oracle Fn which is a container based serverless platform which we can deploy at any cloud or on-premise. We can import our Lambda functions in it and define hot functions for high-performance applications.

It allows us to deploy them using our orchestration tools like Kubernetes, Docker, Mesosphere.

It provides Smart Load Balancer which routes the data to our API according to the traffic load.

It also provides us the ability to extend it and add our custom add-ons in it according to our requirements.

Azure Big Data Serverless

Azure Cloud service has also launched its serverless compute service called Azure Function Service which we can use in various ways to satisfy our needs cost-effectively. It is very much similar to AWS Lambda or Google Cloud Function.

Big Data Serverless in Open Source

Open Faas with Kubernetes

OpenFass (Function as a Service) is a framework for building serverless functions on the top of containers (with docker and kubernetes). With the help of OpenFass, it is easy to turn anything into a serverless function that runs on Linux or windows through Docker or Kubernetes. It provides a built-in functionality such as self-healing infrastructure, auto-scaling and the ability to control every aspect of the cluster.

Conclusion Serverless Architecture

In the world of Big Data, We know that we cannot define a fixed number of resources for our platform because we never know that when the velocity/size of data can change. So Our Big Data Platforms must be able to tackle any these situations, and Serverless Architecture is a very high solution of thinking about these problems.

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