Dattell

Load balancing with Kafka is a straightforward process and is handled by the Kafka producers by default.  While it isn’t traditional load balancing, it does spread out the message load between partitions while preserving messaging ordering. By default, producers choose the partition assignment for each incoming message within a topic, following a round-robin approach. As … Continue reading Load Balancing With Kafka

One aspect of Kafka that can cause some confusion for new users is the consumer offset.  In this post, we define consumer offset and outline the factors that determine the offset. Defining Kafka Consumer Offset The consumer offset is a way of tracking the sequential order in which messages are received by Kafka topics.  Keeping … Continue reading Understanding Kafka Consumer Offset

The California Consumer Privacy Act (CCPA) allows the sale of customer data, even personally identifiable data, but it does add new restrictions.  In this post we will discuss whether or not the new law applies to your organization, the restrictions on selling data without consent, and the restrictions for selling personally identifiable data. If you’re … Continue reading How CCPA Affect’s the Sale of Customer Data

ZooKeeper is used in distributed systems for service synchronization and as a naming registry.  When working with Apache Kafka, ZooKeeper is primarily used to track the status of nodes in the Kafka cluster and maintain a list of Kafka topics and messages. ZooKeeper was originally developed by Yahoo to address the bugs that can arise … Continue reading What is ZooKeeper & How Does it Support Kafka?

Many companies leverage both Apache Kafka and the Elastic Stack (Elasticsearch, Logstash, and Kibana) for log and/or event processing.  Kafka is often used as the transport layer, storing and processing data, typically large amounts of data. Kafka stages data before it makes its way to the Elastic Stack.  Logstash transforms the data, making it uniform. … Continue reading Origins of Kafka and Why it Plays Well With Elasticsearch

Apache Kafka is a high-throughput, open source message queue used by Fortune 100 companies, government entities, and startups alike. Part of Kafka’s appeal is its wide array of use cases.  In this post we will outline several of Kafka’s uses cases from event sourcing to tracking web activities to metrics and more. Use Cases for … Continue reading Kafka Use Cases

The way nodes are organized in an Elasticsearch cluster changes depending on the size of the cluster.  For small, medium, and large Elasticsearch clusters there will be different approaches for optimization. Dattell’s team of engineers are expert at designing, optimizing, and maintaining Elasticsearch implementations and supporting technologies.  Find our more about our Elasticsearch services here. … Continue reading Elasticsearch Optimization for Small, Medium, and Large Clusters

There are six key components to securing Kafka. These best practices will help you optimize Kafka and protect your data from avoidable exposure. #1 Encryption By default, data is plaintext in Kafka, which leaves it vulnerable to a man-in-the-middle attack as data is routed over your network. Transport layer security (TLS) and/or a secure sockets … Continue reading Kafka Optimization: Kafka Security Checklist

Apache Kafka is a distributed system, running in a cluster with each of the nodes referred to as brokers. Kafka topics are partitioned and replicated across the brokers throughout the entirety of the implementation. These partitions allow users to parallelize topics, meaning data for any topic can be divided over multiple brokers. A critical component … Continue reading Kafka Optimization — How many partitions are needed?

There are several message queue programs to choose from: Kafka, RabbitMQ, ActiveMQ, ZeroMQ, Redis, among others. How do you choose which is right for you?

From our real-world experience with Elasticsearch consulting, we found that there are common questions that many new clients have about the technology.
Here are some quick answers to those questions.

Our team is experienced with implementing and fixing Kafka on a wide-range of systems for an even wider-range of business needs. From our real-world experience with Kafka consulting, we found that there are common questions that many new clients have about the technology.
Here are some quick answers to those questions.

We outlined the four primary ways for backing up data and their benefits and drawbacks to help you decide on which approach best meets your company’s needs.

When we are driving, we are routinely making data-driven decisions using the gauges on our dashboard to guide us. Data-driven decision making should be just as easy when it comes to business.

We broke down the thought process for choosing between AWS Elasticsearch and a custom Elasticsearch solution here to help you think through what will be right for you and your team.   

With this guide, you will be able to define the business and technical requirements for your data platform, making the implementation process efficient and successful.

When designing a custom data architecture, business analytics, or operational intelligence platform for a client, four benefits of open source tools make them undoubtedly a better option in the vast majority of cases.

The implementation of a data handling platform, whether it is a centralized reporting system, Business Analytics, Operational Intelligence, or single point of truth for your company, will improve the way you make data-driven decisions.

Dattell is pleased to announce our formal partnership with Elastic, Inc. as an official reseller of the Elastic X-pack.

Dattell is partnering with the creators of Kafka and Confluent to strengthen our commitment to improve messaging and data infrastructure for companies of all sizes.

Issues with Apache Kafka performance are directly tied to system optimization and utilization. Here, we compiled the best practices for a high volume, clustered, general use case.