Style Changes for table, blockquote and image

_config.yml

@@ -138,7 +138,7 @@ jekyll-archives:
     category: '/category/:name/'
 
 # Pagination 
-paginate: 6
+paginate: 9
 paginate_path: /page:num/
 
 # Other

_layouts/default.html

@@ -134,16 +134,17 @@ <h1 class="sitetitle">{{ site.name }}</h1>
 
 </div>
 
+
 <!-- Categories Jumbotron
 ================================================== -->
-<div class="jumbotron fortags">
+<!--<div class="jumbotron fortags">
 	<div class="d-md-flex h-100">
 		<div class="col-md-4 transpdark align-self-center text-center h-100">
             <div class="d-md-flex align-items-center justify-content-center h-100">
                 <h2 class="d-md-block align-self-center py-1 font-weight-light">Explore <span class="d-none d-md-inline">→</span></h2>
             </div>
 		</div>
-		<div class="col-md-8 p-5 align-self-center text-center">
+		 <div class="col-md-8 p-5 align-self-center text-center">
             {% assign categories_list = site.categories %}
             {% if categories_list.first[0] == null %}
                 {% for category in categories_list %}
@@ -155,9 +156,9 @@ <h2 class="d-md-block align-self-center py-1 font-weight-light">Explore <span cl
                 {% endfor %}
             {% endif %}
             {% assign categories_list = nil %}
-		</div>
+		</div> 
 	</div>
-</div>
+</div>-->
 
 <!-- Begin Footer
 ================================================== -->

_posts/2024-06-18-librdkafka-interceptors.md

@@ -6,6 +6,7 @@ categories: [Data, Kafka, Librdkafka]
 image: assets/blog-images/librdkafka_interceptors/KafkaInterceptors.png
 featured: false
 teaser: Building Kafka interceptors for librdkafka clients
+toc: true
 ---
 
 Interceptors in Apache Kafka are plugins that intercept and potentially modify data that is being produced or consumed by Kafka clients. It allows the externalization of common functionality among clients without having to modify the underlying Kafka code. These interceptors can either be producer interceptors or consumer interceptors and act on a single message at a time. Interceptors hook into various stages of a produce or consume request, allowing access to the message and metadata through custom code.

_posts/2024-07-11-primer-on-streaming-databases.md

@@ -6,6 +6,7 @@ categories: [Data, Kafka, Streaming Databases, KSQL, Materialize, RisingWave]
 image: assets/blog-images/streaming_databases/StreamingDatabase.png
 featured: false
 teaser: Why streaming databases are the rage and what makes them the hottest thing in the world of streaming. A deep dive into important characteristics of a few streaming DBs with a comparative lens.
+toc: true
 ---
 
 
@@ -18,7 +19,7 @@ In the modern era of digitalization, data needs to be made available for making
 
 
 
-### Challenges with Stream Processing
+## Challenges with Stream Processing
 
 For the rest of the use cases, we can rely on a stream processing system that processes data in “real-time”. For such real-time processing systems, there are constraints within the system, such as constraints on how quickly the data can be made available for processing from various other systems. It is usually the weakest link in the chain that contributes to the constraints. This makes stream processing in “real-time” a hard problem to solve. 
 
@@ -53,7 +54,7 @@ Although both a stream processing engine and a streaming database serve the same
 Real time analytical databases such as Apache Druid, Apache Pinot, or Clickhouse, on the other hand, are meant for querying data in real-time post ingestion. These databases handle ad-hoc and user facing analytics. Whereas a streaming database is meant to work on data before it is ingested into a RTOLAP database for querying.
 
 
-# Comparing Streaming Databases
+## Comparing Streaming Databases
 
 While evaluating a streaming database, asking a few questions about the features, design, and capabilities can help.
 
@@ -93,7 +94,7 @@ When evaluating any software, the maturity and age of the software is a basic cr
 Finally, understanding the implementation details helps in designing and troubleshooting the stream processing better. An aspect of evaluation is understanding the data flow model, the key primitives, and user facing APIs / constructs.
 
 
-# Comparison of 3 OSS streaming databases
+## Comparison of 3 OSS streaming databases
 
 For this blog post, we will be providing a comparison between three open source databases - KsqlDB, RisingWave, and Materialize. We will briefly compare them across the previously mentioned factors and provide a reference for evaluation.
 

_posts/2024-07-29-Tooling-and-Process-Lessons-from-running-Kafka-at-scale-for-mission-critical-customers.md

@@ -1,7 +1,7 @@
 ---
 layout: post
 title:  "Tooling and Process: Lessons from running Kafka at scale for mission critical customers"
-categories: [Apache Kafka, Mission-Critical Applications, High Availability Systems, Cluster Management, Performance Optimization, Operational Challenges, Kafka at Scale, SRE Practices, ]
+categories: [Apache Kafka, Mission-Critical Applications, High Availability Systems, Cluster Management, Performance, Operational Challenges, Kafka at Scale, SRE Practices, ]
 teaser: Dive into our journey of deploying Apache Kafka at scale for fintech clients, navigating challenges and implementing solutions for mission-critical success
 authors: Arun
 featured: false

_posts/2024-07-29-Why-Real-time-API-Monetization-is-the-need-of-the-day.md

@@ -2,7 +2,7 @@
 layout: post
 title: "Monetizing APIs, Data through usage-based billing"
 authors: ashwin
-categories: [Data, SaaS, API Metering, Billing, Real-time, Cost, Product]
+categories: [Data, SaaS, API Metering, Billing, Real-time, Cost]
 image: assets/blog-images/apinomy_blog/ApinomyBlogTitle.webp
 featured: true
 hidden: true

_posts/2024-08-02-Simplifying-Infrastructure-with-IaC-and-Kafka.md

@@ -1,7 +1,7 @@
 ---
 layout: post
 title:  "From Code to Cluster: Simplifying Infrastructure with IaC and Kafka"
-categories: [Kafka Architecture, Infrastructure as Code (IaC), Automation,DevOps]
+categories: [Kafka Architecture, Infrastructure, Automation,DevOps]
 teaser: Discover how Infrastructure as Code (IaC) can revolutionize your Kafka setup by simplifying management and boosting automation. Learn how to leverage IaC for scalable, error-free Kafka operations and transform your infrastructure practices for optimal efficiency.
 author: Shivaprakash
 image: assets/blog-images/oauth-oidc-blog/IAC.png

_posts/2024-08-02-kafka-on-kubernetes.md

@@ -486,11 +486,11 @@ subgraph KC["Kubernetes Cluster"]
 | | CFK    | Strimzi      | KOperator       |
 | :---        | :---        | :---:         | ---:           |
 | **Workload Type** | StatefulSet        | StrimziPodSet         | Pod           |
-|  |         |          |            |
+
 | **CRs supported**|ClusterLink ConfluentRoleBinding Connector Connect ControlCenter KafkaRestClass KafkaRestProxy| Kafka KafkaBridge KafkaConnector KafkaUser KafkaMirrorMaker2 | KafkaCluster KafkaTopic KafkaUser CruiseControlOperation  |
 | **Networking Models**| Loadbalancer NodePort Ingress      | Loadbalancer NodePort Ingress        | Loadbalancer (envoy or Istio ingress) NodePort Ingress         |
 | **Storage** | * Supports Tiered Storage *   | Generic CSI with PVs     | Generic CSI with PVs |
-|  |         |          |            |
+
 
 
 ### **Security**
@@ -505,7 +505,7 @@ subgraph KC["Kubernetes Cluster"]
 | SASL/OAUTHBEARER | No        | Yes         | No           |
 | SASL/GSSAPI | No        | No         | No           |
 | Kubernetes RBAC | No        | No         | Yes with K8S namespaces and SA           |
-|  |         |          |            |
+
 
 
 ### **Authorization**
@@ -514,7 +514,7 @@ subgraph KC["Kubernetes Cluster"]
 | :---        | :---        | :---:         | ---:           |
 | ACL | Yes        | Yes         | Yes with Envoy           |
 | RBAC | Yes        | No         | No           |
-|  |         |          |            |
+
 
 
 ### **Security**
@@ -525,7 +525,7 @@ subgraph KC["Kubernetes Cluster"]
 | Monitoring add-ons | Control Center, Confluent Health+, JMX        | JMX, Cruise Control         | JMX, Cruise Control           |
 | Disaster recovery | Replicator, ClusterLink. Also support Multi-region clusters        | MirrorMaker2         | MirrorMaker2           |
 | Scale up/out | kubectl scale with Self Balancing        | Non-broker components - K8S HPA and KEDA. Broker - Manual scaling with Strimzi Drain Cleaner         | Autoscaling with Cruise Control           |
-|  |         |          |            |
+
 
 
 # Summary

_posts/2024-08-04-how-to-turn-your-legacy-architecture-to-an-event-driven-system.md

@@ -2,15 +2,16 @@
 layout: post
 title: "Integrating APIs and Event Streaming: for fun and profit"
 authors: avinash
-categories: [Event-Driven Architecture, Kafka, Eventing, command processing, real-time, event driven workflows, saas]
+categories: [Event-Driven Architecture, Kafka, Eventing, Command processing, Real-time, Event Driven Workflows, SaaS]
 image: assets/blog-images/eventception/EventDrivenArchitectureAbstract.jpeg
 featured: true
 hidden: true
 weight: -10
 teaser: "How to progressively get the best out of RESTful and event-driven architectures."
+toc: true
 ---
 
-# How to turn your legacy architecture to an event-driven system
+## How to turn your legacy architecture to an event-driven system
 
 In the world of microservices and real-time applications, event-driven architecture is widely adopted for building loosely coupled systems. As a result of this, multiple patterns have emerged, such as Event Notifications, Event Sourcing, CQRS, etc. All of these patterns depend on the system’s ability to produce events and react to events in a timely manner. But what are these events, and why are they important?
 

_posts/2024-08-11-querying-state-store-in-apache-flink.md

@@ -2,11 +2,12 @@
 layout: post
 title: "Querying Stream Processing Systems (Apache Flink)"
 authors: avinash
-categories: [apache flink, data, stream processing, kafka streams]
+categories: [apache flink, Data, Stream Processing, Kafka Streams]
 image: assets/blog-images/queryable_state_apache_flink/QueryingStateStoreInStreamProcessing.png
 featured: false
 hidden: false
 teaser: "How to query the state store in a stream processing system such as Apache Flink for unifying Operational and Analytical data."
+toc: true
 ---
 
 ## Background

_sass/_starsnonscss.scss

@@ -89,4 +89,17 @@
  .c-rating--small {
    font-size: 50%; }
  .c-rating--big {
-   font-size: 150%; }
+   font-size: 150%; }
+
+
+p:has(img) {
+  padding: 1rem 0;
+  text-align: center; /* Centers content inside <p> tags that contain an <img> */
+}
+
+p:has(img) img {
+  display: inline-block; /* Ensures the image is treated as inline-block */
+}
+.mermaid{
+  padding: 2rem 0;
+}

assets/css/main.scss

@@ -13,3 +13,16 @@
 	"syntax",
     "starsnonscss"
 ;
+
+
+.main-content{
+	table, th, td{
+		border: 1px solid black;
+		border-collapse: collapse;
+		padding: 10px;
+}
+table{
+	width: 100%;
+	margin-bottom: 20px;
+}
+}	

assets/css/screen.css

@@ -472,9 +472,11 @@ span.post-read-more a:hover {
 }
 
 blockquote {
-    border-left: 4px solid #00ab6b;
-    padding: 0 20px;
+    border: 2px solid #00ab6b;
+    padding: 20px;
     font-style: italic;
+    background-color: #c5c5c514;
+    border-radius: 4px;
     color: rgba(0, 0, 0, .5);
 }
 
@@ -1074,4 +1076,10 @@ iframe {
 
 .navbar-collapse{
     flex-grow: 0 !important;
+}
+
+.more-blog{
+    font-size: 14px;
+    padding: 20px;
+    font-weight: bold;
 }