In this installment of podcast, we’re joined by Gunnar Morling (X/Twitter) , from Decodable , a cloud-native stream processing platform that makes it easier to build real-time applications and services, highlights the challenges and opportunities in stream processing, as well as the evolving trends in database and cloud technologies.

Chris Engelbert: Hello everyone. Welcome back to the next episode of simplyblock’s Cloud Commute podcast. Today I have a really good guest, and a really good friend with me. We know each other for quite a while. I don’t know, many, many, many years. Another fellow German. And I guess a lot of, at least when you’re in the Java world, you must have heard of him. You must have heard him. Gunnar, welcome. Happy to have you.

Gunnar Morling: Chris, hello, everybody. Thank you so much, family. Super excited. Yes, I don’t know, to be honest, for how long we have known each other. Yes, definitely quite a few years, you know, always running into each other in the Java community.

Chris Engelbert: Right. I think the German Java community is very encapsulated. There’s a good chance, you know, a good chunk of them.

Gunnar Morling: I mean, you would actively have to try and avoid each other, I guess, if you really don’t want to meet somebody.

Chris Engelbert: That is very, very true. So, well, we already heard who you are, but maybe you can give a little bit of a deeper introduction of yourself.

Gunnar Morling: Sure. So, I’m Gunnar. I work as a software engineer right now at a company called Decodable. We are a small startup in the data streaming space, essentially moving and processing your data. And I think we will talk more about what that means. So, that’s my current role. And I have, you know, a bit of a mixed role between engineering and then also doing outreach work, like doing blog posts, podcasts, maybe sometimes, going to conferences, talking about things. So, that’s what I’m currently doing. Before that, I’ve been for exactly up to the day, exactly for 10 years at Red Hat, where I worked on several projects. So, I started working on different projects from the Hibernate umbrella. Yes, it’s still a thing. I still like it. So, I was doing that for roughly five years working on Bean Validation. I was the spec lead for Bean Validation 2.0, for instance, which I think is also how we met or I believe we interacted somehow with in the context of Bean Validation. I remember something there. And then, well, I worked on a project which is called Debezium. It’s a tool and a platform for change data capture. And again, we will dive into that. But I guess that’s what people might know me for. I’m also a Java champion as you are, Chris. And I did this challenge. I need to mention it. I did this kind of viral challenge in the Java space. Some people might also have come across my name in that context.

Chris Engelbert: All right. Let’s get back to the challenge in a moment. Maybe say a couple of words about Decodable.

Gunnar Morling: Yes. So, essentially, we built a SaaS, a software as a service for stream processing. This means, essentially, it connects to all kinds of data systems, let’s say databases like Postgres or MySQL, streaming platforms like Kafka, Apache Pulsar. It takes data from those kinds of systems. And in the simplest case, it just takes this data and puts it into something like Snowflake, like a search index, maybe another database, maybe S3, maybe something like Apache Pino or Clickhouse. So, it’s about data movement in the simplest case, taking data from one place to another. And very importantly, all this happens in real time. So, it’s not batch driven, like, you know, running once per hour, once per day or whatever. But this happens in near real time. So, not in the hard, you know, computer science sense of the word, with a fixed SLA, but with a very low latency, like seconds, typically. But then, you know, going beyond data movement, there’s also what we would call data processing. So, it’s about filtering your data, transforming it, routing it, joining multiple of those real time data streams, doing things like groupings, real time analytics of this data, so you could gain insight into your data. So, this is what we do. It’s based on Apache Flink as a stream processing engine. It’s based on Debezium as a CDC tool. So, this gives you a source connectivity with all kinds of databases. And yeah, people use it for, as I mentioned, for taking data from one place to another, but then also for, I don’t know, doing fraud detection, gaining insight into their purchase orders or customers, you know, all those kinds of things, really.

Chris Engelbert: All right, cool. Let’s talk about your challenge real quick, because you already mentioned stream processing. Before we go on with, like, the other stuff, like, let’s talk about the challenge. What was that about?

Gunnar Morling: What was that about? Yes, this was, to be honest, it was kind of a random thing, which I started over the holidays between, you know, Christmas and New Year’s Eve. So, this had been on my mind for quite some time, doing something like processing one billion rows, because that’s what it was, a one billion row challenge. And this had been on my mind for a while. And I know somehow, then I had this idea, okay, let me just put it out into the community, and let’s make a challenge out of it and essentially ask people, so how fast can you be with Java to process one billion rows of a CSV file, essentially? And the task was, you know, to take temperature measurements, which were given in that file, and aggregate them per weather station. So, the measurements or the rows in this file were essentially always like, you know, a weather station name and then a temperature value. And you had to aggregate them per station, which means you had to get the minimum, the maximum and the mean value per station. So, this was the task. And then it kind of took off. So, like, you know, many people from the community entered this challenge and also like really big names like Aleksey Shipilëv, Cliff Click, Thomas Wuerthinger, the leads of GraalVm at Oracle and many, many others, they started to work on this and they kept working on it for the entire month of January. And like really bringing down those execution times, essentially, in the end, it was like less than two seconds for processing this file, which I had with 13 gigabytes of size on an eight core CPU configuration.

Chris Engelbert: I think the important thing is he said less than a second, which is already impressive because a lot of people think Java is slow and everything. Right. We know those terms and those claims.

Gunnar Morling: By the way, I should clarify. So, you know, I mean, this is highly parallelizable, right? So, the less than a second number, I think like 350 milliseconds or so this was an old 32 cores I had in this machine with hyperthreading, with turbo boost. So, this was the best I could get.

Chris Engelbert: But it also included reading those, like 13 gigs, right? And I think that is impressive.

Gunnar Morling: Yes. But again, then reading from memory. So, essentially, I wanted to make sure that disk IO is not part of the equation because it would be super hard to measure for me anyway. So, that’s why I said, okay, I will have everything in a RAM disk. And, you know, so everything comes or came out of memory for that context.

Chris Engelbert: Ok. Got it. But still, it got pretty viral. I’ve seen it from the start and I was kind of blown away by who joined that discussion. It was really cool to look after and to just follow up. I didn’t have time to jump into that myself, but by the numbers and the results I’ve seen, I would have not won anyway. That was me not wasting time.

Gunnar Morling: Absolutely. I mean, people pulled off like really crazy tricks to get there. And by the way, if you’re at JavaLand in a few weeks, I will do a talk about some of those things in Java land.

Chris Engelbert: I think by the time this comes out, it was a few weeks ago. But we’ll see.

Gunnar Morling: Ok. I made the mistake for every recording. I made the temporal reference.

Chris Engelbert: That’s totally fine. I think a lot of the JavaLand talks are now recorded these days and they will show up on YouTube. So when this comes out and the talks are already available, I’ll just put it in the show notes.

Gunnar Morling: Perfect.

Chris Engelbert: All right. So that was the challenge. Let’s get back to Decodable. You mentioned Apache Flink being like the underlying technology build on. So how does that work?

Gunnar Morling: So Apache Flink, essentially, that’s an open source project which concerns itself with real-time data processing. So it’s essentially an engine for processing either bounded or unbounded streams of events. So there’s also a way where you could use it in a batch mode. But this is not what we are too interested in so far. It’s always about unbounded data streams coming from a Kafka topic, so it takes those event streams, it defines semantics on those event streams. Like what’s an event time? What does it mean if an event arrives late or out of order? So you have the building blocks for all those kinds of things. Then you have a stack, a layer of APIs, which allow you to implement stream processing applications. So there’s more imperative APIs, which in particular is called the data streaming API. So there you really program in Java, typically, or Scala, I guess, your flow in an imperative way. Yeah Scala, I don’t know who does it, but that may be some people. And then there’s more and more abstract APIs. So there’s a table API, which essentially gives you like a relational programming paradigm. And finally, there’s Flink SQL, which also is what Decodable employs heavily in the product. So there you reason about your data streams in terms of SQL. So let’s say, you know, you want to take the data from an external system, you would express this as a create table statement, and then this table would be backed by a Kafka topic. And you can do a select then from such a table. And then of course you can do, you know, projections by massaging your select clause. You can do filterings by adding where clauses, you can join multiple streams by well using the join operator and you can do windowed aggregations. So I would feel that’s the most accessible way for doing stream processing, because there’s of course, a large number of people who can implement a SQL, right?

Chris Engelbert: Right. And I just wanted to say, and it’s all like a SQL dialect, it’s pretty close as far as I’ve seen to the original like standard SQL.

Gunnar Morling: Yes, exactly. And then there’s a few extensions, you know, because you need to have this notion of event time or what does it mean? How do you express how much lateness you would be willing to accept for an aggregation? So there’s a few extensions like that. But overall, it’s SQL. For my demos, oftentimes, I can start working on Postgres, developing, develop some queries on Postgres, and then I just take them, paste them into like the Flink SQL client, and they might just run as is, or they may need a little bit of adjustment, but it’s pretty much standard SQL.

Chris Engelbert: All right, cool. The other thing you mentioned was the Debezium. And I know you, I think you originally started Debezium. Is that true?

Gunnar Morling: It’s not true. No, I did not start it. It was somebody else at Red Hat, Randall Hauck, he’s now at Confluent. But I took over the project quite early on. So Randall started it. And I know I came in after a few months, I believe. And yeah, I think this is when it really took off, right? So, you know, I went to many conferences, I spoke about it. And of course, others as well. The team grew at Red Hat. So yeah, I was the lead for quite a few years.

Chris Engelbert: So for the people that don’t know, maybe just give a few words about what Debezium is, what it does, and why it is so cool.

Gunnar Morling: Right. Yes. Oh, man, where should I start? In a nutshell, it’s a tool for what’s called change data capture. So this means it taps into the transaction log of your database. And then whenever there’s an insert or an update or delete, it will capture this event, and it will propagate it to consumers. So essentially, you could think about it like the observer pattern for your database. So whenever there’s a data change, like a new customer record gets created, or purchase order gets updated, those kinds of things, you can, you know, react and extract this change event from the database, push it to consumers, either via Kafka or via pullbacks in an API way, or via, you know, Google Cloud PubSub, Kinesis, all those kinds of things. And then well, you can take those events and it enables a ton of use cases. So you know, in the simplest case, it’s just about replication. So taking data from your operational database to your cloud data warehouse, or to your search index, or maybe to cache. But then also people use change data capture for doing things like microservices, data exchange, because I mean, microservices, they, you want to have them self dependent, but still, they need to exchange data, right? So they don’t exist in isolation, and change data capture can help with that in particular, with what’s called the outbox pattern, just on the side note, people use it for splitting up monolithic systems into microservices, you can use this change event stream as an audit log. I mean, if you kind of think about it, it’s, you know, if you just keep those events, all the updates to purchase order, we put them into a database, it’s kind of like a search index, right? Maybe you want to enrich it with a bit of metadata. You can do streaming queries. So I know you maybe you want to spot specific patterns in your data as it changes, and then trigger some sort of alert. That’s the use case, and many, many more, but really, it’s a super versatile tool, I would say.

Chris Engelbert: Yeah, and I also have a couple of talks on that area. And I think my favorite example, that’s something that everyone understands is that you have some order coming in, and now you want to send out invoices. Invoices don’t need to be sent like, in the same operation, but you want to make sure that you only send out the invoice if the invoice was, or if the order was actually generated in the database. So that is where the outbox pattern comes in, or just looking at the order table in general, and filtering out all the new orders.

Gunnar Morling: Yes.

Chris Engelbert: So yeah, absolutely a great tool. Love it. It supports many, many databases. Any idea how many so far?

Gunnar Morling: It keeps growing. I know, certainly 10 or so or more. The interesting thing there is, well, you know, there is not a standardized way you could implement something like Debezium. So each of the databases have their own APIs, formats, their own ways for extracting those change events, which means there needs to be a dedicated Debezium connector for each database, which we want to support. And then the core team, you know, added support for MySQL, Postgres, SQL Server, Oracle, Cassandra, MongoDB, and so on. But then what happened is that also other companies and other organizations picked up the Debezium framework. So for instance, now something like Google Cloud Spanner, it’s also supported via Debezium, because the team at Google decided, that they want to expose change events based on the Debezium event format and infrastructure or ScyllaDB. So they maintain their own CDC connector, but it’s based on Debezium. And the nice thing about that is that it gives you as a user, one unified change event format, right? So you don’t have to care, which is the particular source database, does it come from Cloud Spanner, or does it come from Postgres? You can process those events in a unified way, which I think is just great to see that it establishes itself as a sort of a de facto standard, I would say.

Chris Engelbert: Yeah, I think that is important. That is a very, very good point. Debezium basically defined a JSON and I think Avro standard.

Gunnar Morling: Right. So I mean, you know, it defines the, let’s say, the semantic structure, like, you know, what are the fields, what are the types, how are they organized, and then how you serialize it as Avro, JSON, or protocol buffers. That’s essentially like a pluggable concern.

Chris Engelbert: Right. So we said earlier, Decodable is a cloud platform. So you basically have, in a little bit of a mean term, you have Apache Flink on steroids, ready to use, plus a couple of stuff on top of that. So maybe talk a little bit about that.

Gunnar Morling: Right. So yes, that’s the underlying tech, I would say. And then of course, if you want to put those things into production, there’s so many things you need to consider. Right. So how do you just go about developing and versioning those SQL statements? If you iterate on a statement, you want to have maybe like a preview and get a feeling or maybe just validation of this. So we have all this editing experience, preview. Then maybe you don’t want that all of your users in your organization can access all those streaming pipelines, which you have. Right. So you want to have something like role-based access control. You want to have managed connectors. You want to have automatic provisioning and sizing of your infrastructure. So you don’t want to think too much, “hey, do I need to keep like five machines for this dataflow sitting around?” And what happens if I don’t need them? Do I need to remove them and then scale them back up again? So all this auto scaling, auto provisioning, this is something which we do. Then we will primarily allow you to use SQL to define your queries, but then also we actually let you run your own custom Flink jobs. If that’s something which you want to do, you can do this. We are very close. And again, by the time this will be released, it should be live already. We will have Python, PyFlink support, and yeah, many, many more things. Right. So really it’s a managed experience for those dataflows.

Chris Engelbert: Right. That makes a lot of sense. So let me see. From a user’s perspective, I’m mostly working with SQL. I’m writing my jobs. I’m deploying those. Those jobs are everything from simple ETL to extract, translate, load. What’s the L again?

Gunnar Morling: Load.

Chris Engelbert: There you go. Nobody needs to load data. They just magically appear. But you can also do data enrichment. You said that earlier. You can do joins. Right. So is there anything I have to be aware of that is very complicated compared to just using a standard database?

Gunnar Morling: Yeah. I mean, I think this entire notion of event time, this definitely is something which can be challenging. So let’s say you want to do some sort of windowed analysis, like, you know, how many purchase orders do I have per category and hour, you know, this kind of thing. And now, depending on what’s the source of your data, those events might arrive out of order. Right. So it might be that your hour has closed. But then, like, five minutes later, because some event was stuck in some queue, you still get an event for that past hour. Right. And of course, now the question is, there’s this tradeoff between, okay, how accurate do you want your data to be? Essentially, how long do you want to wait for those late events versus, well, what is your latency? Right. Do you want to get out this updated count at the top of the hour? Or can you afford to wait for those five minutes? So there’s a bit of a tradeoff. I think, you know, this entire complex of event time, I think that’s certainly something where people often have at least some time to learn and grasp the concepts.

Chris Engelbert: Yeah, that’s a very good one. In a previous episode, we had the discussion about connected cars. And connected cars may or may not have an internet connection all the time. So you like super, super late events sometimes. All right. Because we’re almost running out of time.

Gunnar Morling: Wow. Ok.

Chris Engelbert: Yeah. 20 minutes is like nothing. What is the biggest trend you see right now in terms of database, in terms of cloud, in terms of whatever you like?

Gunnar Morling: Right. I mean, that’s a tough one. Well, I guess there can only be one answer, right? It has to be AI. I feel it’s like, I know it’s boring. But well, the trend is not boring. But saying it is kind of boring. But I mean, that’s what I would see. The way I could see this impact things like we do, I mean, it could help you just with like scaling, of course, like, you know, we could make intelligent predictions about what’s your workload like, maybe we can take a look at the data and we can sense, okay, you know, it might make sense to scale out some more compute load already, because we will know with a certain likelihood that it may be needed very shortly. I could see that then, of course, I mean, it could just help you with authoring those flows, right? I mean, with all those LLMs, it might be doable to give you some sort of guided experience there. So that’s a big trend for sure. Then I guess another one, I would see more technical, I feel like that’s a unification happening, right, of systems and categories of systems. So right now we have, you know, databases here, stream processing engines there. And I feel those things might come more closely together. And you would have real-time streaming capabilities also in something like Postgres itself. And I know maybe would expose Postgres as a Kafka broker, in a sense. So I could also see some more, you know, some closer integration of those different kinds of tools.

Chris Engelbert: That is interesting, because I also think that there is a general like movement to, I mean, in the past we had the idea of moving to different databases, because all of them were very specific. And now all of the big databases, Oracle, Postgres, well, even MySQL, they all start to integrate all of those like multi-model features. And Postgres, being at the forefront, having this like super extensibility. So yeah, that would be interesting.

Gunnar Morling: Right. I mean, it’s always going in cycles, I feel right. And even having this trend to decomposition, like it gives you all those good building blocks, which you then can put together and I know create a more cohesive integrated experience, right. And then I guess in five years, we want to tear it apart again, and like, let people integrate everything themselves.

Chris Engelbert: In 5 to 10 years, we have the next iteration of microservices. We called it SOAP, we called it whatever. Now we call it microservices. Who knows what we will call it in the future. All right. Thank you very much. That was a good chat. Like always, I love talking.

Gunnar Morling: Yeah, thank you so much for having me. This was great. Enjoy the conversation. And let’s talk soon.

Chris Engelbert: Absolutely. And for everyone else, come back next week. A new episode, a new guest. And thank you very much. See you.

The post Coding the Cloud: A Dive into Data Streaming with Gunnar Morling from Decodable (video + interview) appeared first on simplyblock.

The rise of stream processing has fundamentally changed how businesses handle real-time data. With the ability to process and analyze continuous streams of data, organizations can make faster, data-driven decisions. Open-source tools have become essential for stream processing, offering powerful solutions to ingest, analyze, and act on data in real time. These tools are critical for optimizing workflows, improving efficiency, and ensuring that businesses stay competitive in a data-driven landscape.

What are the best open-source tools for your stream processing setup?

As the demand for real-time data analysis grows, so does the need for robust and reliable open-source stream processing tools. Developers and engineers are constantly on the lookout for tools that can handle massive volumes of streaming data efficiently. In this post, we’ll explore nine must-know open-source tools for optimizing your stream processing environment.

1. Apache Kafka

Apache Kafka is a distributed event streaming platform used by thousands of companies for building high-performance data pipelines, streaming analytics, and real-time applications. Kafka is well-suited for handling high-throughput, low-latency data streams, and it supports fault tolerance by replicating data across a cluster. It’s the backbone of many modern stream processing architectures

2. Apache Flink

Apache Flink is a stream processing framework for real-time and batch data processing. Its powerful stream-first approach allows it to handle event-time processing and out-of-order data, making it ideal for applications that require accurate, real-time insights. Flink is widely used for complex event-driven applications and real-time analytics.

3. Apache Storm

Apache Storm is a distributed real-time computation system. It processes unbounded streams of data in a fault-tolerant and horizontally scalable manner. Storm is often used for real-time analytics, machine learning, and continuous computation, making it a valuable tool for organizations requiring high-performance stream processing.

4. Apache Samza

Apache Samza is a stream processing framework designed to handle massive volumes of data. Developed by LinkedIn, Samza integrates seamlessly with Apache Kafka and Hadoop, providing robust state management and fault tolerance. Its ability to process real-time streams with low latency makes it a key tool in the stream processing ecosystem.

5. NiFi

Apache NiFi is a dataflow automation tool that supports real-time data stream processing. It allows you to automate the movement of data between systems with ease, enabling users to build complex data pipelines. NiFi’s user-friendly interface and powerful features make it ideal for managing data flows in real-time applications.

6. StreamPipes

StreamPipes is an open-source Industrial IoT (IIoT) analytics platform for processing data streams from various sources. Its easy-to-use pipeline editor allows users to set up stream processing pipelines without writing code. StreamPipes is ideal for businesses looking to process IoT data streams in real time, providing fast insights into sensor data.

7. KSQL (Confluent)

KSQL, a component of the Confluent Platform, is an open-source, SQL-based stream processing engine built on Apache Kafka. It allows developers to write queries that continuously transform and analyze data as it’s ingested. KSQL is widely used for building real-time analytics applications, anomaly detection, and monitoring systems.

8. Logstash

Logstash, part of the Elastic Stack, is an open-source tool for collecting, parsing, and storing data from various sources in real-time. It’s highly flexible and can integrate with a wide range of systems. Logstash’s real-time processing capabilities make it an essential tool for managing large data streams and transforming them into meaningful insights.

9. Esper

Esper is a lightweight, high-performance event stream processing engine that allows you to query streams of events using a SQL-like language. It’s designed for applications where low latency and high throughput are critical, such as financial services, telecommunications, and logistics. Esper excels at detecting patterns and trends in real-time data streams.

Why Choose simplyblock for Stream Processing?

Stream processing platforms excel at handling real-time data analysis, but their performance and reliability ultimately depend on proper infrastructure configuration and resource management. This is where simplyblock’s intelligent orchestration creates unique value:

• Intelligent Infrastructure Optimization: Simplyblock automatically optimizes your stream processing infrastructure across different frameworks (Kafka, Flink, Storm), ensuring optimal performance while reducing operational complexity. The platform handles resource allocation and scaling based on workload patterns.
• Cost-Efficient Resource Management: Simplyblock’s intelligent resource orchestration helps reduce infrastructure costs while maintaining performance. The platform automatically optimizes resource utilization across your streaming stack, preventing over-provisioning while ensuring processing power where needed.
• Simplified Enterprise Management: The Kubernetes-native integration means you can deploy and manage stream processing workflows through standard practices, while simplyblock handles complex infrastructure optimization behind the scenes. Built-in monitoring and automated maintenance ensure reliable stream processing operations.

How to Optimize Stream Processing with Open-source Tools

This guide explored nine essential open-source tools for stream processing, from Apache Kafka for high-performance data pipelines to Esper for complex event processing. While these tools excel at different aspects of stream processing – Flink for stateful processing, Storm for real-time analytics, and Samza for scalability – proper implementation and configuration remain crucial. Tools like NiFi and StreamPipes simplify pipeline creation, while KSQL enables SQL-based stream processing, making real-time analytics more accessible.

If you’re looking to streamline your stream processing operations, simplyblock provides comprehensive solutions that integrate seamlessly with these tools, helping you get the most out of your real-time data pipelines.

Ready to take your stream processing to the next level? Contact simplyblock today to learn how we can help you enhance performance and simplify the management of your data streams.

The post 9 Best Open Source Tools for Stream Processing appeared first on simplyblock.

The world of time-series analytics and predictions is dynamic and continuously evolving. As more organizations gather massive amounts of data, the need for efficient tools to analyze time-series data and make accurate predictions has become paramount. Open-source tools have emerged as essential resources in this domain, offering robust solutions to manage and analyze time-based data efficiently. These tools are crucial for detecting trends, forecasting future values, and automating decision-making processes.

What are the best open-source tools for your time-series analytics setup?

With the growing demand for real-time insights and predictions, the importance of open-source tools in time-series analytics has increased significantly. Developers, data scientists, and analysts are always on the lookout for tools that help them process and predict time-series data with precision. In this post, we will explore nine must-know open-source tools that can help you optimize your time-series analytics and predictions.

1. Prometheus

Prometheus is a powerful open-source system for time-series data collection and storage, widely used for monitoring and alerting. With its ability to efficiently handle high-dimensional data, it allows you to store metrics with timestamps, enabling real-time analysis and predictions. Its integration with visualization tools like Grafana makes it an essential tool for time-series analytics.

2. InfluxDB

InfluxDB is a purpose-built time-series database designed for high-performance handling of time-based data. It excels at ingesting, storing, and analyzing data in real-time, making it perfect for IoT, DevOps monitoring, and application performance metrics. InfluxDB’s query language enables complex analytics, aggregation, and predictions based on time-series data.

3. Grafana

Grafana is an open-source visualization and analytics platform that integrates seamlessly with time-series databases like Prometheus and InfluxDB. It enables users to create rich, interactive dashboards for visualizing time-series data and identifying trends. Its powerful query capabilities make it an excellent tool for monitoring and predictive analytics.

4. Kats (by Facebook)

Kats (Kits to Analyze Time Series) is a lightweight, easy-to-use library developed by Facebook for time-series analysis and predictions. It offers a comprehensive range of features such as forecasting, anomaly detection, and event change detection. Kats simplifies working with time-series data and is highly effective for predictive modeling.

5. Prophet (by Facebook)

Prophet is another tool developed by Facebook, designed for time-series forecasting. It is highly efficient for handling time-series data that contain multiple seasonality with irregular intervals. Prophet’s intuitive interface allows you to quickly generate forecasts with minimal code, making it popular among data scientists for time-series predictions.

6. Druid

Druid is a real-time analytics database designed for fast aggregations and instant data retrieval. It’s ideal for applications that require sub-second query responses on time-series data. Druid offers high scalability and is perfect for analyzing large volumes of time-series data across industries, from digital marketing to IoT.

7. PyCaret

PyCaret is an open-source machine learning library that simplifies time-series forecasting. It automates the process of model selection, training, and evaluation, making it ideal for developers and data scientists who want to quickly build prediction models. PyCaret supports a wide range of algorithms, allowing users to perform robust time-series analysis with ease.

8. OpenTSDB

OpenTSDB is a scalable, distributed time-series database designed for high-throughput data. It enables the collection, storage, and retrieval of billions of data points in real-time, making it suitable for IoT, infrastructure monitoring, and predictive maintenance. OpenTSDB integrates with popular tools like Hadoop for large-scale time-series analysis.

9. Apache Flink

Apache Flink is a stream processing framework that excels at processing time-series data in real-time. With Flink’s stateful streaming, it can handle large-scale, time-based data streams and make predictions on-the-fly. It’s highly versatile, offering advanced features such as windowing, event time, and out-of-order processing, making it ideal for real-time analytics and predictions.

Why Choose simplyblock for Time-Series Analytics?

Time-series databases require specialized storage engines and query optimizations to handle the unique characteristics of temporal data. This is where SimplyBlock’s intelligent orchestration creates unique value:

• Intelligent Time-Series Optimization: Simplyblock implements specialized storage strategies for time-series workloads. The platform optimizes time-based partitioning and data layout while employing efficient compression algorithms specifically designed for timestamp-value pairs. It manages automated downsampling and retention policies, implements smart caching for recent time windows and hot data, and maintains high-speed ingestion buffers with intelligent batch processing to maximize throughput.
• Performance-Optimized Query Engine: Simplyblock manages the complex aspects of time-series query processing by implementing parallel processing of time-range queries and efficient time-based indexing strategies. The platform handles automated aggregation and rollup management, optimizes scan operations for sequential time-based access, and provides smart query routing based on time partitions to ensure optimal performance.
• Enterprise-Grade Time-Series Management: Through Kubernetes integration, simplyblock automates critical operational aspects of time-series management. This includes sophisticated time-based sharding and rebalancing, precise multi-node timestamp synchronization, and efficient high-cardinality series handling. The platform provides comprehensive real-time monitoring of time-series metrics and implements automated backup systems with flexible time-based recovery points for robust data protection.

How to Optimize Time-Series Analytics with Open-source Tools

This guide explored nine essential open-source tools for time-series analytics, from Prometheus’s metrics collection to Apache Flink’s stream processing capabilities. While these tools excel at different aspects – InfluxDB for high-speed ingestion, Prophet for forecasting, and OpenTSDB for scalability – proper implementation is crucial. Tools like Grafana enable visualization, while specialized libraries like Kats and PyCaret simplify predictive modeling. Each tool offers unique capabilities for handling temporal data patterns and time-based queries.

If you’re looking to further streamline your time-series analytics and predictions, simplyblock offers comprehensive solutions that integrate seamlessly with these tools, helping you get the most out of your time-series data processing.

Ready to optimize your time-series analytics? Contact simplyblock today to discover how we can help you enhance your data analysis, performance, and scalability.

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