is kafka a nosql database

Full Answer

Is Apache Kafka a good fit for your database?

This blog post explains the idea behind databases and different features like storage, queries, and transactions to evaluate when Kafka is a good fit and when it is not. Jay Kreps, the co-founder of Apache Kafka and Confluent, explained in 2017 why “it's okay to store data in Apache Kafka.”

Should you store data long-term in Kafka?

One good reason to store data long-term in Kafka is to be able to use the data at a later point in time for processing, correlations, or analytics. Query and Processing - Can You Consume and Analyze the Kafka Storage?

Is “Kafka as a database” a one-size-fits-all solution?

There is never a one-size-fits-all solution in software development. The discussions surrounding “Kafka As A Database” are deep and I encourage everyone to dig further into the opinions of both Team Red and Team Blue.

What type of database is Kafka?

Kafka concepts like partitions, replication, guaranteed messaging order, and distributed commit logs make it fit into the Atomicity, Consistency, Isolation, Durability (ACID) transaction properties of a database. On that basis, one can regard Apache Kafka as a type of hybrid database that provides ACID guarantees.

What are the 4 types of NoSQL databases?

Understanding differences in the four types of NoSQL databasesDocument databases.Key-value stores.Column-oriented databases.Graph databases.

What is the difference between Kafka and MongoDB?

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. On the other hand, MongoDB is detailed as "The database for giant ideas".

Which are NoSQL databases?

NoSQL databases store data in documents rather than relational tables. Accordingly, we classify them as "not only SQL" and subdivide them by a variety of flexible data models. Types of NoSQL databases include pure document databases, key-value stores, wide-column databases, and graph databases.

Which is not a NoSQL database?

Which of the following is not a NoSQL database? Explanation: Microsoft SQL Server is a relational database management system developed by Microsoft.

Is Hadoop a NoSQL database?

Hadoop is not a type of database, but rather a software ecosystem that allows for massively parallel computing. It is an enabler of certain types NoSQL distributed databases (such as HBase), which can allow for data to be spread across thousands of servers with little reduction in performance.

Is Kafka a database?

Apache Kafka is a database. It provides ACID guarantees and is used in hundreds of companies for mission-critical deployments. However, in many cases, Kafka is not competitive to other databases.

Does Kafka use MongoDB?

Kafka streams data from MongoDB in a hassle-free manner. Enterprises need to publish and send high volumes of data to many other subscribers or manage multiple streams to MongoDB. MongoDB is widely used in the industry, and Kafka helps developers layout multiple asynchronous streams using Kafka MongoDB Connection.

What is Kafka used for?

Kafka is used to build real-time streaming data pipelines and real-time streaming applications. A data pipeline reliably processes and moves data from one system to another, and a streaming application is an application that consumes streams of data.

What is the best NoSQL database?

As the most efficient NoSQL database option for websites and API endpoints, MongoDB is easy to get started with, and it can grow and evolve with your website. MongoDB already has excellent integration with popular web programming languages like Python, PHP, Node, Java, Golang, and many others.

Which is the simplest NoSQL databases?

Key-value stores are the simplest NoSQL databases. Every single item in a key value database is stored as an attribute name (or "key") together with its value.

Does Amazon use NoSQL?

Web giants such as Amazon, Google, and Facebook have long used NoSQL databases to help manage their own online operations, and their work inspired a slew of NoSQL open source projects, including Cassandra and MongoDB.

What are the four basic categories of NoSQL databases provide examples of each category and describe a use case?

NoSQL Databases are mainly categorized into four types: Key-value pair, Column-oriented, Graph-based and Document-oriented. Every category has its unique attributes and limitations. None of the above-specified database is better to solve all the problems. Users should select the database based on their product needs.

What are the types of NoSQL databases Mcq?

What are the types of nosql databases. Document databases. Graph & Column-oriented databases.

What are three examples of a NoSQL database choose three?

Explanation: MongoDB, Apache Cassandra, and Redis are examples of a NoSQL database. The Hadoop Distributed File System (HDFS), Ceph, and GlusterFS are examples of distributed file systems (DFS).

Is SQL Server a NoSQL database?

SQL databases are primarily called as Relational Databases (RDBMS); whereas NoSQL database are primarily called as non-relational or distributed database....Difference between SQL and NoSQL.SQLNoSQLExamples: MySQL, PostgreSQL, Oracle, MS-SQL Server etcExamples: MongoDB, GraphQL, HBase, Neo4j, Cassandra etc6 more rows•Jun 6, 2022

What Do You Think? Is Apache Kafka a Database (for Some Use Cases)?

I know this is a controversial discussion. What are your thoughts? As explained above, I am not recommending that you replace your existing databases. But sometimes Kafka alone is sufficient, isn't it?

What is ksqldb in Kafka?

Kafka Streams and ksqlDB - the event streaming database for Kafka - allow us to build stateful streaming applications, including powerful concepts like joins, sliding windows, and interactive queries of the state.

What Is a Database? Oracle? NoSQL? Hadoop?

Let’s think about the term “database” at a very high level. According to Wikipedia:

Why is Kafka client side important?

The importance of Kafka’s client-side is crucial for the discussion of potentially replacing a database because Kafka applications can be stateless or stateful; the latter keeping state in the application instead of using an external database . The storage section below contains more details about how the client application can store data long term and make it highly available.

What is Kafka stream?

In Kafka Streams applications, that solves the problem of abstracting access to local stable storage instead of using an external database. Using an external database would require external communication/RPC every time an event is processed - a clear anti-pattern in event streaming architectures.

Why is NoSQL used in database?

In the 2000s, non-relational databases became popular, referred to as NoSQL because they use different query languages.".

What is a database system?

Often, the term "database" is also used to loosely refer to any of the DBMS, the database system or an application associated with the database. Computer scientists may classify database-management systems according to the database models that they support. Relational databases became dominant in the 1980s.

Overview

With Kafka Connect, we are able to set up Kafka to interact with external data stores. In this article, we will be using source connectors to monitor and retrieve data from the configured traditional data sources.

Prerequisites

Have Confluent running with Connectors component configured with the appropriate connector plugins.

JDBC (PostgreSQL) Setup

RDBMS databses are connected using JDBC, so the configurations remain similar across engines when creating a new connector:

NoSQL (MongoDB) Setup

NoSQL connectors are database engine dependent, so the setup from one engine will differ to another. For this article, we’re using MongoDB as an example.

How to use Kafka?

In the article, Team Red argues that the data integrity features of databases perform the crucial functionality of “access control.” A common architecture setup is to use Kafka alongside a Change Data Capture (CDC) system. In the diagram above, there are three basic steps: 1 User requests hit the application and kick off transactional write operations to the database. 2 If all the checks look good, the writes land and update the database. 3 CDC reacts accordingly, translates the valid changes into valid events, and ships them off to Kafka to be consumed.

Who reviews ACID properties of a database transaction and how they can be implemented with a stream-based architecture?

In one of his presentations, Martin Kleppmann reviews the ACID properties of a database transaction and how they can be implemented with a stream-based architecture:

What is the property of state in a database?

How can a stream replace this concept that we are so used to? If streams are a never-ending series of immutable events, then the property of state is the culmination of events up to a certain point in time. For example, the latest state of a database is the culmination of all operations made to that database. In computing, this is referred to as Event Sourcing. This paradigm is very different to how many developers are used to thinking about their data.

Can you use Kafka as a database?

Can you use Kafka as a database? Team Blue says yes .

Is it expensive to write in Kafka?

This means that writes can be very expensive . If your application is bottlenecking around expensive writes, the conventional database may hold you back. Team Red doesn’t mention that putting a database in front of Kafka would cause you to miss out on one of Kafka’s best qualities.

Is Kafka a one size fits all solution?

There is never a one-size-fits-all solution in software development. The discussions surrounding “Kafka As A Database” are deep and I encourage everyone to dig further into the opinions of both Team Red and Team Blue.

Can you write a full-fledged database management system?

However, during that process you will eventually confront every hard problem that database management systems have faced for decades. You will more or less have to write a full-fledged DB MS in your application code. And you will probably not do a great job, because databases take years to get right. You will have to deal with dirty reads, phantom reads, write skew, and all the other symptoms of a hastily implemented database.

Step 1: Setup stream and database connections

Before we can do anything interesting we first have to setup our stream and database connections. We'll use the following two options that relate to fault tolerance:

Step 2: Consume records from the stream

To consume records from a stream, you first poll the stream. This gives you a collection of ConsumerRecords which you then iterate through in order to access each individual stream record. This is standard Kafka API stuff, and it looks like this:

Step 3: Convert streamed records to JSON

Before we write consumer records to the database, we need to put each record in a format that has columns. In our example we’re streaming byte arrays, which by themselves have no field related attributes, so we need to convert these byte arrays into a type containing attributes that will correspond to columns in our database.

Step 4: Persist the JSON document to HPE Ezmeral Data Fabric Document Database

This part is easy. We can insert each JSON document as a new row to a table in MapR Database with one line of code, like this:

Step 5: Update the stream cursor

Finally, we’ll update the cursor in our stream topic. In the unlikely event that our stream consumer fails, this ensures that a new consumer will be able to continue working from where the last consumer left off.

Summary

The design we just outlined provides a scalable approach to persisting stream data. It ensures thread-safety by processing immutable stream data with idempotent stream consumers and achieves fault tolerance by updating stream cursors only after records have been persisted in HPE Ezmeral Data Fabric Document Database.

Why would I need it?

Let’s imagine that you have many different databases, which store contexts of different applications or microservices in your ecosystem. Sooner or later you undestand that the contexts of the applications must be in sync. For example, customers data should usually be available for most of them.

Data organization options

In almost every relatively big distributed system there are many different applications, which master different types of data. And in most cases, when a new business case appears, only some subset of application data is needed to be available for a target system for this particular case.

How about we start already?

But enough of this foreword, and let’s assume you know how to organize your data optimally in your systems.

Kafka Connect

Kafka Connect is an additional layer of abstraction between Kafka and external data sources. Kafka Connect cluster consists of one or more worker nodes. Each worker provides a RESTful API for you to manage connectors within them. When you instantiate a connector, one or more tasks are started for it.

Low-level Data Poller

The second option would be to implement your own Data Poller application in Java or another programming language leveraging provided Producer or Consumer APIs. This approach is much more programmatic and may seem tempting, especially when you already have Java expertise in your team.

Third Party Solution

Another way would be to go for a third-party solution, such as Qlik Replicate or IBM Infosphere Data Replication platform. They may provide you with all the necessary functionality and even more. However, such platforms may have both benefits and drawbacks.

Overview

Prerequisites

JDBC (Postgresql) Setup

NoSQL (Mongodb) Setup

Running The System

References