Timely (and Stateful) Processing with Apache Beam (2023)

In a prior blogpost, Iintroduced the basics of stateful processing in Apache Beam, focusing on theaddition of state to per-element processing. So-called timely processingcomplements stateful processing in Beam by letting you set timers to request a(stateful) callback at some point in the future.

What can you do with timers in Beam? Here are some examples:

  • You can output data buffered in state after some amount of processing time.
  • You can take special action when the watermark estimates that you havereceived all data up to a specified point in event time.
  • You can author workflows with timeouts that alter state and emit output inresponse to the absence of additional input for some period of time.

These are just a few possibilities. State and timers together form a powerfulprogramming paradigm for fine-grained control to express a huge variety ofworkflows. Stateful and timely processing in Beam is portable across dataprocessing engines and integrated with Beam’s unified model of event timewindowing in both streaming and batch processing.

What is stateful and timely processing?

In my prior post, I developed an understanding of stateful processing largelyby contrast with associative, commutative combiners. In this post, I’llemphasize a perspective that I had mentioned only briefly: that elementwiseprocessing with access to per-key-and-window state and timers represents afundamental pattern for “embarrassingly parallel” computation, distinct fromthe others in Beam.

In fact, stateful and timely computation is the low-level computational patternthat underlies the others. Precisely because it is lower level, it allows youto really micromanage your computations to unlock new use cases and newefficiencies. This incurs the complexity of manually managing your state andtimers - it isn’t magic! Let’s first look again at the two primarycomputational patterns in Beam.

Element-wise processing (ParDo, Map, etc)

The most elementary embarrassingly parallel pattern is just using a bunch ofcomputers to apply the same function to every input element of a massivecollection. In Beam, per-element processing like this is expressed as a basicParDo - analogous to “Map” from MapReduce - which is like an enhanced “map”,“flatMap”, etc, from functional programming.

The following diagram illustrates per-element processing. Input elements aresquares, output elements are triangles. The colors of the elements representtheir key, which will matter later. Each input element maps to thecorresponding output element(s) completely independently. Processing may bedistributed across computers in any way, yielding essentially limitlessparallelism.

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This pattern is obvious, exists in all data-parallel paradigms, and hasa simple stateless implementation. Every input element can be processedindependently or in arbitrary bundles. Balancing the work between computers isactually the hard part, and can be addressed by splitting, progress estimation,work-stealing, etc.

Per-key (and window) aggregation (Combine, Reduce, GroupByKey, etc.)

The other embarassingly parallel design pattern at the heart of Beam is per-key(and window) aggregation. Elements sharing a key are colocated and thencombined using some associative and commutative operator. In Beam this isexpressed as a GroupByKey or Combine.perKey, and corresponds to the shuffleand “Reduce” from MapReduce. It is sometimes helpful to think of per-keyCombine as the fundamental operation, and raw GroupByKey as a combiner thatjust concatenates input elements. The communication pattern for the inputelements is the same, modulo some optimizations possible for Combine.

In the illustration here, recall that the color of each element represents thekey. So all of the red squares are routed to the same location where they areaggregated and the red triangle is the output. Likewise for the yellow andgreen squares, etc. In a real application, you may have millions of keys, sothe parallelism is still massive.

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The underlying data processing engine will, at some level of abstraction, usestate to perform this aggregation across all the elements arriving for a key.In particular, in a streaming execution, the aggregation process may need towait for more data to arrive or for the watermark to estimate that all inputfor an event time window is complete. This requires some way to store theintermediate aggregation between input elements as well a way to a receive acallback when it is time to emit the result. As a result, the execution ofper key aggregation by a stream processing engine fundamentally involves stateand timers.

However, your code is just a declarative expression of the aggregationoperator. The runner can choose a variety of ways to execute your operator.I went over this in detail in my prior post focused on state alone. Since you do notobserve elements in any defined order, nor manipulate mutable state or timersdirectly, I call this neither stateful nor timely processing.

Per-key-and-window stateful, timely processing

Both ParDo and Combine.perKey are standard patterns for parallelism that goback decades. When implementing these in a massive-scale distributed dataprocessing engine, we can highlight a few characteristics that are particularlyimportant.

Let us consider these characteristics of ParDo:

  • You write single-threaded code to process one element.
  • Elements are processed in arbitrary order with no dependenciesor interaction between processing of elements.

And these characteristics for Combine.perKey:

  • Elements for a common key and window are gathered together.
  • A user-defined operator is applied to those elements.

Combining some of the characteristics of unrestricted parallel mapping andper-key-and-window combination, we can discern a megaprimitive from which webuild stateful and timely processing:

(Video) Stateful processing of massive out of order streams with Apache Beam

  • Elements for a common key and window are gathered together.
  • Elements are processed in arbitrary order.
  • You write single-threaded code to process one element or timer, possiblyaccessing state or setting timers.

In the illustration below, the red squares are gathered and fed one by one tothe stateful, timely, DoFn. As each element is processed, the DoFn hasaccess to state (the color-partitioned cylinder on the right) and can settimers to receive callbacks (the colorful clocks on the left).

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So that is the abstract notion of per-key-and-window stateful, timelyprocessing in Apache Beam. Now let’s see what it looks like to write code thataccesses state, sets timers, and receives callbacks.

Example: Batched RPC

To demonstrate stateful and timely processing, let’s work through a concreteexample, with code.

Suppose you are writing a system to analyze events. You have a ton of datacoming in and you need to enrich each event by RPC to an external system. Youcan’t just issue an RPC per event. Not only would this be terrible forperformance, but it would also likely blow your quota with the external system.So you’d like to gather a number of events, make one RPC for them all, and thenoutput all the enriched events.


Let’s set up the state we need to track batches of elements. As each elementcomes in, we will write the element to a buffer while tracking the number ofelements we have buffered. Here are the state cells in code:

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new DoFn<Event, EnrichedEvent>() { @StateId("buffer") private final StateSpec<BagState<Event>> bufferedEvents = StateSpecs.bag(); @StateId("count") private final StateSpec<ValueState<Integer>> countState = StateSpecs.value();  TBD }

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class StatefulBufferingFn(beam.DoFn): BUFFER_STATE = BagStateSpec('buffer', EventCoder()) COUNT_STATE = CombiningValueStateSpec('count', VarIntCoder(), combiners.SumCombineFn())

Walking through the code, we have:

  • The state cell "buffer" is an unordered bag of buffered events.
  • The state cell "count" tracks how many events have been buffered.

Next, as a recap of reading and writing state, let’s write our @ProcessElementmethod. We will choose a limit on the size of the buffer, MAX_BUFFER_SIZE. Ifour buffer reaches this size, we will perform a single RPC to enrich all theevents, and output.

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new DoFn<Event, EnrichedEvent>() { private static final int MAX_BUFFER_SIZE = 500; @StateId("buffer") private final StateSpec<BagState<Event>> bufferedEvents = StateSpecs.bag(); @StateId("count") private final StateSpec<ValueState<Integer>> countState = StateSpecs.value(); @ProcessElement public void process( ProcessContext context, @StateId("buffer") BagState<Event> bufferState, @StateId("count") ValueState<Integer> countState) { int count = firstNonNull(countState.read(), 0); count = count + 1; countState.write(count); bufferState.add(context.element()); if (count >= MAX_BUFFER_SIZE) { for (EnrichedEvent enrichedEvent : enrichEvents(bufferState.read())) { context.output(enrichedEvent); } bufferState.clear(); countState.clear(); } }  TBD }
(Video) Stateful Processing of Clickstream Events using Apache Beam with Jainik Vora

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class StatefulBufferingFn(beam.DoFn): MAX_BUFFER_SIZE = 500; BUFFER_STATE = BagStateSpec('buffer', EventCoder()) COUNT_STATE = CombiningValueStateSpec('count', VarIntCoder(), combiners.SumCombineFn()) def process(self, element, buffer_state=beam.DoFn.StateParam(BUFFER_STATE), count_state=beam.DoFn.StateParam(COUNT_STATE)): buffer_state.add(element) count_state.add(1) count = count_state.read() if count >= MAX_BUFFER_SIZE: for event in buffer_state.read(): yield event count_state.clear() buffer_state.clear()

Here is an illustration to accompany the code:

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  • The blue box is the DoFn.
  • The yellow box within it is the @ProcessElement method.
  • Each input event is a red square - this diagram just shows the activity fora single key, represented by the color red. Your DoFn will run the sameworkflow in parallel for all keys which are perhaps user IDs.
  • Each input event is written to the buffer as a red triangle, representingthe fact that you might actually buffer more than just the raw input, eventhough this code doesn’t.
  • The external service is drawn as a cloud. When there are enough bufferedevents, the @ProcessElement method reads the events from state and issuesa single RPC.
  • Each output enriched event is drawn as a red circle. To consumers of thisoutput, it looks just like an element-wise operation.

So far, we have only used state, but not timers. You may have noticed thatthere is a problem - there will usually be data left in the buffer. If no moreinput arrives, that data will never be processed. In Beam, every window hassome point in event time when any further input for the window is consideredtoo late and is discarded. At this point, we say that the window has “expired”.Since no further input can arrive to access the state for that window, thestate is also discarded. For our example, we need to ensure that all leftoverevents are output when the window expires.

Event Time Timers

An event time timer requests a call back when the watermark for an inputPCollection reaches some threshold. In other words, you can use an event timetimer to take action at a specific moment in event time - a particular point ofcompleteness for a PCollection - such as when a window expires.

For our example, let us add an event time timer so that when the window expires,any events remaining in the buffer are processed.

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new DoFn<Event, EnrichedEvent>() {  @TimerId("expiry") private final TimerSpec expirySpec = TimerSpecs.timer(TimeDomain.EVENT_TIME); @ProcessElement public void process( ProcessContext context, BoundedWindow window, @StateId("buffer") BagState<Event> bufferState, @StateId("count") ValueState<Integer> countState, @TimerId("expiry") Timer expiryTimer) { expiryTimer.set(window.maxTimestamp().plus(allowedLateness));  same logic as above  } @OnTimer("expiry") public void onExpiry( OnTimerContext context, @StateId("buffer") BagState<Event> bufferState) { if (!bufferState.isEmpty().read()) { for (EnrichedEvent enrichedEvent : enrichEvents(bufferState.read())) { context.output(enrichedEvent); } bufferState.clear(); } }}

Timely (and Stateful) Processing with Apache Beam (10)

(Video) State & timers patterns in Apache Beam

class StatefulBufferingFn(beam.DoFn):  EXPIRY_TIMER = TimerSpec('expiry', TimeDomain.WATERMARK) def process(self, element, w=beam.DoFn.WindowParam, buffer_state=beam.DoFn.StateParam(BUFFER_STATE), count_state=beam.DoFn.StateParam(COUNT_STATE), expiry_timer=beam.DoFn.TimerParam(EXPIRY_TIMER)): expiry_timer.set(w.end + ALLOWED_LATENESS)  same logic as above  @on_timer(EXPIRY_TIMER) def expiry(self, buffer_state=beam.DoFn.StateParam(BUFFER_STATE), count_state=beam.DoFn.StateParam(COUNT_STATE)): events = buffer_state.read() for event in events: yield event buffer_state.clear() count_state.clear()

Let’s unpack the pieces of this snippet:

  • We declare an event time timer with @TimerId("expiry"). We will use theidentifier "expiry" to identify the timer for setting the callback time aswell as receiving the callback.

  • The variable expiryTimer, annotated with @TimerId, is set to the valueTimerSpecs.timer(TimeDomain.EVENT_TIME), indicating that we want acallback according to the event time watermark of the input elements.

  • In the @ProcessElement element we annotate a parameter @TimerId("expiry") Timer. The Beam runner automatically provides this Timer parameter by whichwe can set (and reset) the timer. It is inexpensive to reset a timerrepeatedly, so we simply set it on every element.

  • We define the onExpiry method, annotated with @OnTimer("expiry"), thatperforms a final event enrichment RPC and outputs the result. The Beam runnerdelivers the callback to this method by matching its identifier.

Illustrating this logic, we have the diagram below:

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Both the @ProcessElement and @OnTimer("expiry") methods perform the sameaccess to buffered state, perform the same batched RPC, and output enrichedelements.

Now, if we are executing this in a streaming real-time manner, we might stillhave unbounded latency for particular buffered data. If the watermark is advancingvery slowly, or event time windows are chosen to be quite large, then a lot oftime might pass before output is emitted based either on enough elements orwindow expiration. We can also use timers to limit the amount of wall-clocktime, aka processing time, before we process buffered elements. We can choosesome reasonable amount of time so that even though we are issuing RPCs that arenot as large as they might be, it is still few enough RPCs to avoid blowing ourquota with the external service.

Processing Time Timers

A timer in processing time (time as it passes while your pipeline is executing)is intuitively simple: you want to wait a certain amount of time and thenreceive a call back.

To put the finishing touches on our example, we will set a processing timetimer as soon as any data is buffered. Note that we set the timer only whenthe current buffer is empty, so that we don’t continually reset the timer.When the first element arrives, we set the timer for the current moment plusMAX_BUFFER_DURATION. After the allotted processing time has passed, acallback will fire and enrich and emit any buffered elements.

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new DoFn<Event, EnrichedEvent>() {  private static final Duration MAX_BUFFER_DURATION = Duration.standardSeconds(1); @TimerId("stale") private final TimerSpec staleSpec = TimerSpecs.timer(TimeDomain.PROCESSING_TIME); @ProcessElement public void process( ProcessContext context, BoundedWindow window, @StateId("count") ValueState<Integer> countState, @StateId("buffer") BagState<Event> bufferState, @TimerId("stale") Timer staleTimer, @TimerId("expiry") Timer expiryTimer) { if (firstNonNull(countState.read(), 0) == 0) { staleTimer.offset(MAX_BUFFER_DURATION).setRelative(); }  same processing logic as above  } @OnTimer("stale") public void onStale( OnTimerContext context, @StateId("buffer") BagState<Event> bufferState, @StateId("count") ValueState<Integer> countState) { if (!bufferState.isEmpty().read()) { for (EnrichedEvent enrichedEvent : enrichEvents(bufferState.read())) { context.output(enrichedEvent); } bufferState.clear(); countState.clear(); } }  same expiry as above }

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class StatefulBufferingFn(beam.DoFn):  STALE_TIMER = TimerSpec('stale', TimeDomain.REAL_TIME) MAX_BUFFER_DURATION = 1 def process(self, element, w=beam.DoFn.WindowParam, buffer_state=beam.DoFn.StateParam(BUFFER_STATE), count_state=beam.DoFn.StateParam(COUNT_STATE), expiry_timer=beam.DoFn.TimerParam(EXPIRY_TIMER), stale_timer=beam.DoFn.TimerParam(STALE_TIMER)): if count_state.read() == 0: # We set an absolute timestamp here (not an offset like in the Java SDK) stale_timer.set(time.time() + StatefulBufferingFn.MAX_BUFFER_DURATION)  same logic as above  @on_timer(STALE_TIMER) def stale(self, buffer_state=beam.DoFn.StateParam(BUFFER_STATE), count_state=beam.DoFn.StateParam(COUNT_STATE)): events = buffer_state.read() for event in events: yield event buffer_state.clear() count_state.clear()
(Video) Watermarks: Time and Progress in Apache Beam and Beyond

Here is an illustration of the final code:

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Recapping the entirety of the logic:

  • As events arrive at @ProcessElement they are buffered in state.
  • If the size of the buffer exceeds a maximum, the events are enriched and output.
  • If the buffer fills too slowly and the events get stale before the maximum is reached,a timer causes a callback which enriches the buffered events and outputs.
  • Finally, as any window is expiring, any events buffered in that window areprocessed and output prior to the state for that window being discarded.

In the end, we have a full example that uses state and timers to explicitlymanage the low-level details of a performance-sensitive transform in Beam. Aswe added more and more features, our DoFn actually became pretty large. Thatis a normal characteristic of stateful, timely processing. You are reallydigging in and managing a lot of details that are handled automatically whenyou express your logic using Beam’s higher-level APIs. What you gain from thisextra effort is an ability to tackle use cases and achieve efficiencies thatmay not have been possible otherwise.

State and Timers in Beam’s Unified Model

Beam’s unified model for event time across streaming and batch processing hasnovel implications for state and timers. Usually, you don’t need to do anythingfor your stateful and timely DoFn to work well in the Beam model. But it willhelp to be aware of the considerations below, especially if you have usedsimilar features before outside of Beam.

Event Time Windowing “Just Works”

One of the raisons d'être for Beam is correct processing of out-of-order eventdata, which is almost all event data. Beam’s solution to out-of-order data isevent time windowing, where windows in event time yield correct results nomatter what windowing a user chooses or what order the events come in.

If you write a stateful, timely transform, it should work no matter how thesurrounding pipeline chooses to window event time. If the pipeline choosesfixed windows of one hour (sometimes called tumbling windows) or windows of 30minutes sliding by 10 minutes, the stateful, timely transform shouldtransparently work correctly.

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This works in Beam automatically, because state and timers are partitioned perkey and window. Within each key and window, the stateful, timely processing isessentially independent. As an added benefit, the passing of event time (akaadvancement of the watermark) allows automatic release of unreachable statewhen a window expires, so you often don’t have to worry about evicting oldstate.

Unified real-time and historical processing

A second tenet of Beam’s semantic model is that processing must be unifiedbetween batch and streaming. One important use case for this unificationis the ability to apply the same logic to a stream of events in real time andto archived storage of the same events.

A common characteristic of archived data is that it may arrive radically out oforder. The sharding of archived files often results in a totally differentordering for processing than events coming in near-real-time. The data willalso all be all available and hence delivered instantaneously from the point ofview of your pipeline. Whether running experiments on past data or reprocessingpast results to fix a data processing bug, it is critically important that yourprocessing logic be applicable to archived events just as easily as incomingnear-real-time data.

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It is (deliberately) possible to write a stateful and timely DoFn that deliversresults that depend on ordering or delivery timing, so in this sense there isadditional burden on you, the DoFn author, to ensure that this nondeterminismfalls within documented allowances.

Go use it!

I’ll end this post in the same way I ended the last. I hope you will go try outBeam with stateful, timely processing. If it opens up new possibilities foryou, then great! If not, we want to hear about it. Since this is a new feature,please check the capability matrix to see the level of support foryour preferred Beam backend(s).

And please do join the Beam community atuser@beam.apache.org and follow@ApacheBeam on Twitter.


What is PCollection and PTransform in dataflow? ›

A PCollection can contain either a bounded or unbounded number of elements. Bounded and unbounded PCollections are produced as the output of PTransforms (including root PTransforms like Read and Create ), and can be passed as the inputs of other PTransforms.

What is the difference between Apache Beam and airflow? ›

Airflow shines in data orchestration and pipeline dependency management, while Beam is a unified tool for building big data pipelines, which can be executed in the most popular data processing systems such as Spark or Flink.

Is dataflow same as Apache Beam? ›

Cloud Dataflow: Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem.

Does dataflow use Apache Beam? ›

The Apache Beam programming model simplifies the mechanics of large-scale data processing. Using one of the Apache Beam SDKs, you build a program that defines the pipeline. Then, one of Apache Beam's supported distributed processing backends, such as Dataflow, executes the pipeline.

What is difference between Dataproc and Dataflow? ›

Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem. Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine.

What is PTransform in Apache Beam? ›

A PTransform is an object describing (not executing) a computation. The actual execution semantics for a transform is captured by a runner object. A transform object always belongs to a pipeline object.

Is airflow and dataflow same? ›

Airflow is a platform to programmatically author, schedule, and monitor workflows. Cloud Dataflow is a fully-managed service on Google Cloud that can be used for data processing. You can write your Dataflow code and then use Airflow to schedule and monitor Dataflow job.

What is the difference between Kafka and beam? ›

Beam is an API that uses an underlying stream processing engine like Flink, Storm, etc... in one unified way. Kafka is mainly an integration platform that offers a messaging system based on topics that standalone applications use to communicate with each other.

What is Apache Beam vs spark? ›

Apache Beam means a unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines in multiple execution environments. Apache Spark defines as a fast and general engine for large-scale data processing.

Is Apache Beam an ETL tool? ›

You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.

What is SDK in Apache Beam? ›

The Apache Beam SDK is an open source programming model for data pipelines. You define these pipelines with an Apache Beam program and can choose a runner, such as Dataflow, to execute your pipeline.

Is Apache Beam MapReduce? ›

In 2014, Google releases Cloud Dataflow, a programming model in SDK for writing data-parallel processing pipelines and a fully managed service for executing them. Apache Beam (2015) developed from a number of internal Google technologies, including MapReduce, FlumeJava, and Millwheel.

How do you deploy your Apache Beam pipeline in Google Cloud Dataflow? ›

After you construct and test your Apache Beam pipeline, you can use the Dataflow managed service to deploy and execute it. Once on the Dataflow service, your pipeline code becomes a Dataflow job.
These include the following:
  1. Parallelization and distribution. ...
  2. Optimization. ...
  3. Automatic tuning features.

What is ParDo in Apache Beam? ›

ParDo is the core element-wise transform in Apache Beam, invoking a user-specified function on each of the elements of the input PCollection to produce zero or more output elements, all of which are collected into the output PCollection .

What service account does Dataflow use? ›

Access to Dataflow is governed by Google service accounts. A service account is used by the Dataprep by Trifacta application to access services and resources in the Google Cloud Platform. A service account can be used by one or more users, who are accessing the platform.

Is Dataproc an ETL tool? ›

For example, you can use Dataproc to effortlessly ETL terabytes of raw log data directly into BigQuery for business reporting. Managed — Use Spark and Hadoop clusters without the assistance of an administrator or special software.

What is Dataproc used for? ›

Dataproc is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks. Use Dataproc for data lake modernization, ETL, and secure data science, at scale, integrated with Google Cloud, at a fraction of the cost.

What is difference between pipeline and Dataflow? ›

Data moves from one component to the next via a series of pipes. Data flows through each pipe from left to right. A "pipeline" is a series of pipes that connect components together so they form a protocol.

What is CoGroupByKey used for? ›

Aggregates all input elements by their key and allows downstream processing to consume all values associated with the key. While GroupByKey performs this operation over a single input collection and thus a single type of input values, CoGroupByKey operates over multiple input collections.

How many times will be the process Processelement method of a DoFn called? ›

Example 1: ParDo with a simple DoFn

The process method is called once per element, and it can yield zero or more output elements.

What is ParDo in Java? ›

Javadoc. A transform for generic parallel processing. A ParDo transform considers each element in the input PCollection , performs some processing function (your user code) on that element, and emits zero or more elements to an output PCollection.

What is the difference between cloud composer and dataflow? ›

Cloud Composer is a cross platform orchestration tool that supports AWS, Azure and GCP (and more) with management, scheduling and processing abilities. Cloud Dataflow handles tasks. Cloud Composer manages entire processes coordinating tasks that may involve BigQuery, Dataflow, Dataproc, Storage, on-premises, etc.

Is spark a dataflow? ›

Oracle Cloud Infrastructure (OCI) Data Flow is a fully managed Apache Spark service that performs processing tasks on extremely large datasets—without infrastructure to deploy or manage. Developers can also use Spark Streaming to perform cloud ETL on their continuously produced streaming data.

Is AWS glue similar to Airflow? ›

Airflow can be classified as a tool in the "Workflow Manager" category, while AWS Glue is grouped under "Big Data Tools". Some of the features offered by Airflow are: Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.

What is a runner in beam? ›

The Direct Runner executes pipelines on your machine and is designed to validate that pipelines adhere to the Apache Beam model as closely as possible.

What is bounded and unbounded data? ›

Bounded data is finite; it has a beginning and an end. Unbounded data is an ever-growing, essentially infinite data set. The distinction is independent of how the data is processed. Often, unbounded data is equated to stream processing and bounded data to batch processing, but this is starting to change.

What Kafka streams? ›

Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in an Apache Kafka® cluster. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.

Is Apache beam widely used? ›

Apache Beam is widely used at Google, according to Kerry Donny-Clark, engineering manager for Apache Beam at Google. During a keynote on July 18, Clark noted that Google uses Beam to support data processing for YouTube, Waze, the Vertex AI machine learning platform and the Google Dataplex data fabric.

Does Apache beam use spark? ›

The Apache Spark Runner can be used to execute Beam pipelines using Apache Spark. The Spark Runner can execute Spark pipelines just like a native Spark application; deploying a self-contained application for local mode, running on Spark's Standalone RM, or using YARN or Mesos.

What is Beam big data? ›

Introduction to Apache Beam

Apache Beam is a unified programming model for batch and streaming data processing jobs. It provides a software development kit to define and construct data processing pipelines as well as runners to execute them.

What is the difference between Kafka and airflow? ›

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. Airflow belongs to "Workflow Manager" category of the tech stack, while Kafka can be primarily classified under "Message Queue".

Is Luigi better than airflow? ›

Airflow's UI is also far superior to Luigi's, which is frankly minimal. With Airflow, you can see and interact with running tasks and executions much better than you can with Luigi. When it comes to restarting and rerunning pipelines, Luigi again has its pros and cons.

Is Prefect better than airflow? ›

Both Airflow and Prefect can be set up using pip, docker or other containerisation options. However, Prefect is very well organised and is probably more extensible out-of-the-box. To run Airflow, you'll need a scheduler and webserver, but AWS and GCP both provide managed services for the platform.

Can airflow replace Jenkins? ›

Airflow vs Jenkins: Production and Testing

Since Airflow is not a DevOps tool, it does not support non-production tasks. This means that any job you load on Airflow will be processed in real-time. However, Jenkins is more suitable for testing builds.

What is the difference between Kafka and spark streaming? ›

Kafka analyses the events as they unfold. As a result, it employs a continuous (event-at-a-time) processing model. Spark, on the other hand, uses a micro-batch processing approach, which divides incoming streams into small batches for processing.

Is Apache airflow an orchestration tool? ›

By January of 2019, Airflow was announced as a Top-Level Apache Project by the Foundation and is now considered the industry's leading workflow orchestration solution.

What can I use Apache airflow for? ›

What is Airflow Used For? Apache Airflow is used for the scheduling and orchestration of data pipelines or workflows. Orchestration of data pipelines refers to the sequencing, coordination, scheduling, and managing complex data pipelines from diverse sources.

Is airflow a BPM? ›

Airbnb is a fast growing, data informed company.

Is airflow similar to Autosys? ›

User Interface: Airflow provides a good graphical interface where you can monitor and admin DAGs. In terms of functionality this looks similar to other scheduling tools ( like Cronacle, Autosys) UI .

Why is airflow so popular? ›

The advantage of using Airflow over other workflow management tools is that Airflow allows you to schedule and monitor workflows, not just author them. This outstanding feature enables enterprises to take their pipelines to the next level.

Why is Apache Airflow better? ›

Benefits of Apache Airflow include: Ease of use—you only need a little python knowledge to get started. Open-source community—Airflow is free and has a large community of active users. Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc).

Are prefects free? ›

Yes, Prefect offers a free plan. Learn more about Prefect pricing.

How do I create a dynamic DAG in Airflow? ›

One method for dynamically generating DAGs is to have a single Python file which generates DAGs based on some input parameter(s) (e.g. a list of APIs or tables). A common use case for this is an ETL or ELT-type pipeline where there are many data sources or destinations.

Can Airflow be used for ETL? ›

Airflow ETL is one such popular framework that helps in workflow management. It has excellent scheduling capabilities and graph-based execution flow makes it a great alternative for running ETL jobs.

Is Airflow a CI CD tool? ›

Since Airflow and all its components are defined in source code, it is a fitting approach to create a robust development and deployment framework with CI/CD tools.

Can Jenkins be used for ETL? ›

Earlier we were using Jenkins to build our ETL pipelines. Jenkins is an automation server used for continuous-integration and continuous-deployment (CI/CD).


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