Traditional relational databases have been the typical go-to option for data storage requirements over the years. However, with expanding data volumes and evolving applications, there was a need for more flexible, scalable, and high-performance solutions. This led to the emergence of NoSQL databases like DynamoDB and Elasticsearch.
While both options present you with distinct advantages, the choice mainly hinges on your specific needs and use cases. This could be anything from fast search and analytics across large data volumes to applications requiring high-performance and low-latency data access at any scale.
A DynamoDB vs Elasticsearch comparison of crucial parameters will enable you to choose the one that best fits your needs. So, let’s get started!
DynamoDB: A Quick Overview
DynamoDB is a managed NoSQL database service provided by Amazon Web Services (AWS). This cloud-native platform doesn’t run on-premises or even in a hybrid cloud; it runs on AWS. As a result, you don’t have to invest in hardware to scale.
The serverless nature of DynamoDB avoids the need for you to provision any servers or manage, patch, install, operate, or maintain any software. This facilitates zero downtime maintenance in DynamoDB.
Key Terms in DynamoDB
- Tables: Data in DynamoDB is stored in tables, which serve as containers for items. Each table is associated with a primary key to uniquely identify each item in the table.
- Items: The individual records in DynamoDB tables are called items; each item consists of one or more attributes.
- Attributes: Attributes are the key-value pairs that make an item in a table. For attributes, DynamoDB supports scalar, document, and set data types.
- Primary Key: A primary key is used to uniquely identify each item in a table. Primary keys in DynamoDB are of two types: partition key and composite key (combination of partition key and sort key).
Key Features of DynamoDB
- Multi-active Replication: DynamoDB utilizes global tables, allowing you to replicate your data across the AWS regions of your choice. This multi-active replication involves replicating ongoing data changes to all the tables in the specified regions, providing 99.999% availability.
- Impressive Performance: With its optimization for high-performance workloads and APIs encouraging efficient database usage, DynamoDB delivers single-digit millisecond performance at any scale.
- Supports Zero-ETL Integration: DynamoDB supports zero-ETL integration with Amazon OpenSearch Service and Amazon Redshift. You can use these integrations for complex analytics and advanced search on your DynamoDB table data without any impact on production workloads.
- On-demand Backup and Restore: You can create full backups of any-size DynamoDB tables for long-term retention and archival for regulatory compliance requirements. To automatically schedule, copy, tag, and manage the lifecycle of your DynamoDB on-demand backups, you can use the AWS Backup integration service.
- Change Data Capture (CDC): DynamoDB supports CDC for near-real-time streaming of item-level changes, including creates, updates, or deletes. It offers two CDC streaming models, DynamoDB Streams and Kinesis Data Streams for DynamoDB, and is ideal for applications with event-driven architecture.
Elasticsearch: A Quick Overview
Elasticsearch is a distributed open-source search engine built on Apache Lucene. It is a part of the larger ELK (Elasticsearch, Logstash, and Kibana) stack, which is a set of open-source tools for data ingestion, storage, enrichment, analysis, and visualization.
You can use Elasticsearch with varied data types, including structured, unstructured, and time-based data. It is designed to store, search, and analyze large volumes of such data in near-real time with milli-second responses. The main reason Elasticsearch is able to achieve fast search responses is that it searches an index instead of searching the text directly. An Elasticsearch index is a logical namespace used to store data in an optimized format.
Key Terms in Elasticsearch
- Inverted Index: An index in Elasticsearch is called an inverted index. It’s a data structure that enables fast and efficient full-text searches. This data structure stores a mapping from content, such as numbers or words, to its locations in a document or set of documents.
- Cluster: A cluster is a group of Elasticsearch nodes working together for the distribution of data and processing tasks.
- Shard: A shard is a partition of an Elasticsearch index. This allows data distribution across multiple nodes for improved performance and fault tolerance.
Key Features of Elasticsearch
- Horizontal Scalability: By including additional nodes to your cluster in Elasticsearch, you can increase its capacity and reliability. When you add more nodes to a cluster, Elasticsearch automatically reorganizes itself to evenly distribute the data across the available nodes.
- Full-Text Search: Elasticsearch supports full-text searches by using a structure called an inverted index at its core. This involves mapping search terms to documents containing those terms, facilitating fast information retrieval.
- Plugin and Integration Support: Elasticsearch is compatible with several plugins and integrations. This includes security plugins, API extensions, alerting tools, data recovery integrations, and more, making it easily adaptable for your enterprise’s needs.
- Auto-Completion: The auto-complete feature of Elasticsearch makes it easy for you to improve your search provision and find relevant results as you type your queries.
- Support for Multiple Coding Languages: Elasticsearch supports multiple coding languages, including Python, Java, PHP, .NET, Plus, and more. This makes it extremely adaptable and approachable to developers.
DynamoDB vs Elasticsearch
Data Model
DynamoDB uses a key-value store with document support for a primary data model; you can store data as flexible JSON documents in DynamoDB. For efficient retrieval, these documents are identified by primary keys (hashes and ranges). With a flexible schema, each item can have different attributes.
The data is partitioned and replicated across several availability zones within an AWS region. As a result, this fully managed, serverless NoSQL database is suitable for applications requiring consistent, single-digit millisecond latency at any scale.
On a similar note, Elasticsearch is also schema-flexible and document-oriented, allowing you to store data in a flexible JSON document format. It supports complex nested data structures and involves automatically indexing the data for fast search and retrieval. While Elasticsearch can store data, its main focus is on lightning-fast search capabilities and real-time data analysis.
You can deploy Elasticsearch as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud).
Use Case
DynamoDB is well-suited for mobile, web, ad tech, gaming, and IoT, among other applications. It’s developed to support applications requiring access patterns with item-level isolation. DynamoDB also supports ACID transactions for such applications.
On the contrary, Elasticsearch is suitable for use cases involving complex queries, such as fuzzy matching, free text searches, or analytics-based queries. It can be of significant use if you want to analyze log or event data or if you require a powerful full-text search engine. You can also use Elasticsearch with other components of the ELK stack for security analytics. This involves monitoring network traffic, identifying potential threats, and detecting anomalies.
Factors to Consider When Choosing DynamoDB or Elasticsearch
Among the different factors that can help you choose between DynamoDB vs Elasticsearch, the critical ones are scalability, performance, cost, and integration capabilities.
DynamoDB vs Elasticsearch Scalability
DynamoDB supports horizontal scalability. It involves automatically distributing data across partitions (shards) and replicas across multiple Availability Zones (AZs). This provides you with fault tolerance and high availability for your data.
Similar to DynamoDB, Elasticsearch achieves similar scalability by adding nodes to clusters. The nodes can be shards (holding data partitions) or coordinating nodes (for cluster management). Such a distributed architecture can accommodate increasing data volumes and query demands for elastic scaling.
DynamoDB vs Elasticsearch Performance
DynamoDB facilitates blazing speeds for your reads and writes. With its in-memory storage and solid-state drives (SSDs), DynamoDB delivers low-latency performance with predictable response times.
In the event of any failures, including servers, networks, software, or operating systems, DynamoDB provides useful strategies. You can use timeouts, retries, and backoff to improve resilience. It also offers Amazon DynamoDB Accelerator (DAX) to cache popular items if your applications are read-heavy. DAX is capable of delivering requests, even at millions of requests per second, in as low as sub-millisecond latencies.
On the other hand, Elasticsearch works well for search queries and real-time analytics. It provides impressively fast information retrieval and analysis with its full-text search capabilities and aggregation features. This is possible since Elasticsearch mainly relies on the filesystem cache to make search fast. However, due to Elasticsearch’s focus on indexing and searching, its write performance isn’t as fast as DynamoDB.
Elasticsearch vs DynamoDB Cost
DynamoDB offers two pricing models: on-demand capacity and provisioned capacity. For on-demand capacity, you needn’t specify how much read and write throughput your applications will perform. You pay per request for the reads and writes your application performs on your tables. This is a good pricing model if your applications handle fluctuating or unpredictable traffic.
The second model, which is the provisioned capacity, involves you specifying the number of reads and writes per second that your application requires. You will be charged on the provisioned capacity amount. This model is well-suited if your applications deal with predictable traffic or gradually increasing traffic.
On the contrary, Elasticsearch provides an open-source option and can be self-hosted without any licensing fees. This makes it a cost-effective option if you’re on a budget. However, it involves commercial subscriptions if you require premium features, especially for functionalities like security and alerting. For this, Elasticsearch offers a managed cloud service called Elastic Cloud. It involves pricing tiers based on storage, computing, and support.
Simplify Data Integration with DynamoDB or Elasticsearch Using Airbyte
Both DynamoDB and Elasticsearch present you with distinct features, making each one suitable for varied use cases. Based on whether you require the real-time data storage of DynamoDB or the advanced search functionalities of Elasticsearch, you can choose accordingly. Regardless of your choice, it is essential to integrate your data with these platforms for effective utilization.
For an almost effortless integration process, you can opt for Airbyte, an efficient data movement platform. With its 400+ connectors, including source and destination options, you can move data from varied sources into DynamoDB or Elasticsearch. It’s also a good choice to move your DynamoDB data to your desired destination, including loading data from DynamoDB to Elasticsearch.
Here are some other note-worthy features of Airbyte:
- Change Data Capture (CDC): With Airbyte’s CDC capabilities, you can identify incremental changes made to your source data and replicate them to your destination. This allows you to keep track of updates and ensure data consistency.
- Custom Transformations Using dbt: You can use Airbyte’s dbt Cloud integration to create and run transformations right after your data syncs in Airbyte Cloud. This enables you to transform raw data into a suitable format for analysis and reporting.
- Supports Popular Vector Databases: Airbyte supports popular vector databases like Chroma, Pinecone, Qdrant, Weavite, and Milvus, among others. You can load semi-structured or unstructured data directly into the vector store destinations to simplify your AI workflows.
- Automated Pipeline Setup with Terraform: To manage Airbyte resources, such as sources, destinations, and connections, you can use the Airbyte Terraform Provider. It allows you to use Terraform, an IaC solution, to define and provision infrastructure through code.
Final Thoughts
The DynamoDB vs Elasticsearch comparison presents you with the unique features of each platform that helps you choose the appropriate option for your use case.
DynamoDB, the NoSQL database service, is an optimal choice if your applications demand high read/write throughput for real-time operations. However, Elasticsearch is the better choice if your applications involve complex queries with filtering, sorting, and aggregations.
Whether you opt for DynamoDB or Elasticsearch or an integration of both, you can use Airbyte to plug your data into these platforms. This will help you utilize the features of the chosen platform to your advantage.