#ApacheSpark

[yahooeng]

Benchmarking Streaming Computation Engines at Yahoo!

(Yahoo Storm Team in alphabetical order) Sanket Chintapalli, Derek Dagit, Bobby Evans, Reza Farivar, Tom Graves, Mark Holderbaugh, Zhuo Liu, Kyle Nusbaum, Kishorkumar Patil, Boyang Jerry Peng and Paul Poulosky.

DISCLAIMER: Dec 17th 2015 data-artisans has pointed out to us that we accidentally left on some debugging in the flink benchmark. So the flink numbers should not be directly compared to the storm and spark numbers.  We will rerun and repost the numbers when we have fixed this.

UPDATE: Dec 18, 2015 there was a miscommunication and the code that was checked in was not the exact code we ran with for flink.  The real code had the debugging removed.  Data-Artisans has looked at the code and confirmed it and the current numbers are good.  We will still rerun at some point soon.

Executive Summary -  Due to a lack of real-world streaming benchmarks, we developed one to compare Apache Flink, Apache Storm and Apache Spark Streaming. Storm 0.10.0, 0.11.0-SNAPSHOT and Flink 0.10.1 show sub- second latencies at relatively high throughputs with Storm having the lowest 99th percentile latency. Spark streaming 1.5.1 supports high throughputs, but at a relatively higher latency.

At Yahoo, we have invested heavily in a number of open source big data platforms that we use daily to support our business. For streaming workloads, our platform of choice has been Apache Storm, which replaced our internally developed S4 platform. We have been using Storm extensively, and the number of nodes running Storm at Yahoo has now reached about 2,300 (and is still growing).

Since our initial decision to use Storm in 2012, the streaming landscape has changed drastically. There are now several other noteworthy competitors including Apache Flink, Apache Spark (Spark Streaming), Apache Samza, Apache Apex and Google Cloud Dataflow. There is increasing confusion over which package offers the best set of features and which one performs better under which conditions (for instance see here, here, here, and here).

To provide the best streaming tools to our internal customers, we wanted to know what Storm is good at and where it needs to be improved compared to other systems. To do this we started to look for stream processing benchmarks that we could use to do this evaluation, but all of them were lacking in several fundamental areas. Primarily, they did not test with anything close to a real world use case. So we decided to write one and released it as open source https://github.com/yahoo/streaming-benchmarks.  In our initial evaluation we decided to limit our test to three of the most popular and promising platforms (Storm, Flink and Spark), but welcome contributions for other systems, and to expand the scope of the benchmark.

Benchmark Design

The benchmark is a simple advertisement application. There are a number of advertising campaigns, and a number of advertisements for each campaign. The job of the benchmark is to read various JSON events from Kafka, identify the relevant events, and store a windowed count of relevant events per campaign into Redis. These steps attempt to probe some common operations performed on data streams.

The flow of operations is as follows (and shown in the following figure):

Read an event from Kafka.

Deserialize the JSON string.

Filter out irrelevant events (based on event_type field)

Take a projection of the relevant fields (ad_id and event_time)

Join each event by ad_id with its associated campaign_id. This information is stored in Redis.

Take a windowed count of events per campaign and store each window in Redis along with a timestamp of the time the window was last updated in Redis. This step must be able to handle late events.

The input data has the following schema:

user_id: UUID

page_id: UUID

ad_id: UUID

ad_type: String in {banner, modal, sponsored-search, mail, mobile}

event_type: String in {view, click, purchase}

event_time: Timestamp

ip_address: String

Producers create events with timestamps marking creation time. Truncating this timestamp to a particular digit gives the begin-time of the time window the event belongs in. In Storm and Flink, updates to Redis are written periodically, but frequently enough to meet a chosen SLA. Our SLA was 1 second, so once per second we wrote updated windows to Redis. Spark operated slightly differently due to great differences in its design. There’s more details on that in the Spark section. Along with the data, we record the time at which each window in Redis was last updated.

After each run, a utility reads windows from Redis and compares the windows’ times to their last_updated_at times, yielding a latency data point. Because the last event for a window cannot have been emitted after the window closed but will be very shortly before, the difference between a window’s time and its last_updated_at time minus its duration represents the time it took for the final tuple in a window to go from Kafka to Redis through the application.

window.final_event_latency = (window.last_updated_at – window.timestamp) – window.duration

This is a bit rough, but this benchmark was not purposed to get fine-grained numbers on these engines, but to provide a more high-level view of their behavior.

Benchmark setup

10 second windows

1 second SLA

100 campaigns

10 ads per campaign

5 Kafka nodes with 5 partitions

1 Redis node

10 worker nodes (not including coordination nodes like Storm’s Nimbus)

5-10 Kafka producer nodes

3 ZooKeeper nodes

Since the Redis node in our architecture only performs in-memory lookups using a well-optimized hashing scheme, it did not become a bottleneck. The nodes are homogeneously configured, each with two Intel E5530 processors running at 2.4GHz, with a total of 16 cores (8 physical, 16 hyperthreading) per node. Each node has 24GiB of memory, and the machines are all located within the same rack, connected through a gigabit Ethernet switch. The cluster has a total of 40 nodes available.

We ran multiple instances of the Kafka producers to create the required load since individual producers begin to fall behind at around 17,000 events per second. In total, we use anywhere between 20 to 25 nodes in this benchmark.

The use of 10 workers for a topology is near the average number we see being used by topologies internal to Yahoo. Of course, our Storm clusters are larger in size, but they are multi-tenant and run many topologies.

To begin the benchmarks Kafka is cleared, Redis is populated with initial data (ad_id to campaign_id mapping), the streaming job is started, and then after a bit of time to let the job finish launching, the producers are started with instructions to produce events at a particular rate, giving the desired aggregate throughput. The system was left to run for 30 minutes before the producers were shut down. A few seconds were allowed for all events to be processed before the streaming job itself was stopped. The benchmark utility was then run to generate a file containing a list of window.last_updated_at – window.timestamp numbers. These files were saved for each throughput we tested and then were used to generate the charts in this document.

Flink

The benchmark for Flink was implemented in Java by using Flink’s DataStream API.  The Flink DataStream API has many similarities to Storm’s streaming API.  For both Flink and Storm, the dataflow can be represented as a directed graph. Each vertex is a user defined operator and each directed edge represents a flow of data.  Storm’s API uses spouts and bolts as its operators while Flink uses map, flatMap, as well as many pre-built operators such as filter, project, and reduce. Flink uses a mechanism called checkpointing to guarantee processing which offers similar guarantees to Storm’s acking. Flink has checkpointing off by default and that is how we ran this benchmark. Notable configs we used in Flink is listed below:

taskmanager.heap.mb: 15360

taskmanager.numberOfTaskSlots: 16

The Flink version of the benchmark uses the FlinkKafkaConsumer to read data in from Kafka.  The data read in from Kafka—which is in a JSON formatted string— is then deserialized and parsed by a custom defined flatMap operator. Once deserialized, the data is filtered via a custom defined filter operator. Afterwards, the filtered data is projected by using the project operator. From there, the data is joined with data in Redis by a custom defined flapMap function. Lastly, the final results are calculated from the data and written to redis.

The rate at which Kafka emitted data events into the Flink benchmark is varied from 50,000 events/sec to 170,000 events/sec. For each Kafka emit rate, the percentile latency for a tuple to be completely processed in the Flink benchmark is illustrated in the graph below.

The percentile latency for all Kafka emit rates are relatively the same. The percentile latency rises linearly until around the 99th percentile, where the latency appears to increase exponentially. 

Spark

For the Spark benchmark, the code was written in Scala. Since the micro-batching methodology of Spark is different than the pure streaming nature of Storm, we needed to rethink parts of the benchmark. Storm and Flink benchmarks would update the Redis database once a second to try and meet our SLA, keeping the intermediate update values in a local cache. As a result, the batch duration in the Spark streaming version was set to 1 second, at least for smaller amounts of traffic. We had to increase the batch duration for larger throughputs.

The benchmark is written in a typical Spark style using DStreams. DStreams are the streaming equivalent of regular RDDs, and create a separate RDD for every micro batch. Note that in the subsequent discussion, we use the term “RDD” instead of “DStream” to refer to the RDD representation of the DStream in the currently active microbatch. Processing begins with the direct Kafka consumer included with Spark 1.5. Since the Kafka input data in our benchmark is stored in 5 partitions, this Kafka consumer creates a DStream with 5 partitions as well. After that, a number of transformations are applied on the DStreams, including maps and filters. The transformation involving joining data with Redis is a special case. Since we do not want to create a separate connection to Redis for each record, we use a mapPartitions operation that can give control of a whole RDD partition to our code.  This way, we create one connection to Redis and use this single connection to query information from Redis for all the events in that RDD partition. The same approach is used later when we update the final results in Redis.

It should be noted that our writes to Redis were implemented as a side-effect of the execution of the RDD transformation in order to keep the benchmark simple, so this would not be compatible with exactly-once semantics.

We found that with high enough throughput, Spark was not able to keep up.  At 100,000 messages per second the latency greatly increased. We considered adjustments along two control dimensions to help Spark cope with increasing throughput.

The first is the microbatch duration. This is a control dimension that is not present in a pure streaming system like Storm. Increasing the duration increases latency while reducing overhead and therefore increasing maximum throughput. The challenge is that the choice of the optimal batch duration that minimizes latency while allowing spark to handle the throughput is a time consuming process. Essentially, we have to set a batch duration, run the benchmark for 30 minutes, check the results and decrease/increase the duration.

The second dimension is parallelism. However, increasing parallelism is simpler said than done in the case of Spark. For a true streaming system like Storm, one bolt instance can send its results to any number of subsequent bolt instances by using a random shuffle. To scale, one can increase the parallelism of the second bolt. In the case of a micro batch system like Spark, we need to perform a reshuffle operation similar to how intermediate data in a Hadoop MapReduce program are shuffled and merged across the cluster. But the reshuffling itself introduces considerable overhead. Initially, we thought our operations were CPU-bound, and so the benefits of reshuffling to a higher number of partitions would outweigh the cost of reshuffling.  Instead, we found the bottleneck to be scheduling, and so reshuffling only added overhead. We suspect that at higher throughput rates or with operations that are CPU-bound, the reverse would be true.

The final results are interesting. There are essentially three behaviors for a Spark workload depending on the window duration. First, if the batch duration is set sufficiently large, the majority of the events will be handled within the current micro batch. The following figure shows the resulting percentile processing graph for this case (100K events, 10 seconds batch duration).

But whenever 90% of events are processed in the first batch, there is possibility of improving latency. By reducing the batch duration sufficiently, we get into a region where the incoming events are processed within 3 or 4 subsequent batches. This is the second behavior, in which the batch duration puts the system on the verge of falling behind, but is still manageable, and results in better latency. This situation is shown in the following figure for a sample throughput rate (100K events, 3 seconds batch duration).

Finally, the third behavior is when Spark streaming falls behind. In this case, the benchmark takes a few minutes after the input data finishes to process all of the events. This situation is shown in the following figure. Under this undesirable operating region, Spark spills lots of data onto disks, and in extreme cases we could end up running out of disk space.

One final note is that we tried the new back pressure feature introduced in Spark 1.5. If the system is in the first operating region, enabling back pressure does nothing. In the second operating region, enabling back pressure results in longer latencies. The third operating region is where back pressure shows the most negative impact.  It changes the batch length, but Spark still cannot cope with the throughput and falls behind. This is shown in the next figures. Our experiments showed that the current back pressure implementation did not help our benchmark, and as a result we disabled it.

Performance without back pressure (top), and with back pressure enabled (bottom). The latencies with the back pressure enabled are worse (70 seconds vs 120 seconds). Note that both of these results are unacceptable for a streaming system as both fall behind the incoming data. Batch duration was set to 2 seconds for each run, with 130,000 throughput.

Storm

Storm’s benchmark was written using the Java API. We tested both Apache Storm 0.10.0 release and a 0.11.0 snapshot. The snapshot’s commit hash was a8d253a. One worker process per host was used, and each worker was given 16 tasks to run in 16 executors - one for each core.

Storm 0.10.0:

Storm 0.11.0:

Storm compared favorably to both Flink and Spark Streaming. Storm 0.11.0 beat Storm 0.10.0, showing the optimizations that have gone in since the 0.10.0 release. However, at high-throughput both versions of Storm struggled. Storm 0.10.0 was not able to handle throughputs above 135,000 events per second.

Storm 0.11.0 performed similarly until we disabled acking. In the benchmarking topology, acking was used for flow control but not for processing guarantees. In 0.11.0, Storm added a simple back pressure controller, allowing us to avoid the overhead of acking. With acking enabled, 0.11.0 performed terribly at 150,000/s—slightly better than 0.10.0, but still far worse than anything else. With acking disabled, Storm even beat Flink for latency at high throughput. However, with acking disabled, the ability to report and handle tuple failures is disabled also.

Conclusions and Future Work

It is interesting to compare the behavior of these three systems. Looking at the following figure, we can see that Storm and Flink both respond quite linearly. This is because these two systems try to process an incoming event as it becomes available. On the other hand, the Spark Streaming system behaves in a stepwise function, a direct result from its micro-batching design.

The throughput vs latency graph for the various systems is maybe the most revealing, as it summarizes our findings with this benchmark. Flink and Storm have very similar performance, and Spark Streaming, while it has much higher latency, is expected to be able to handle much higher throughput.

We did not include the results for Storm 0.10.0 and 0.11.0 with acking enabled beyond 135,000 events per second, because they could not keep up with the throughput. The resulting graph had the final point for Storm 0.10.0 in the 45,000 ms range, dwarfing every other line on the graph. The longer the topology ran, the higher the latencies got, indicating that it was losing ground.

All of these benchmarks except where otherwise noted were performed using default settings for Storm, Spark, and Flink, and we focused on writing correct, easy to understand programs without optimizing each to its full potential. Because of this each of the six steps were a separate bolt or spout. Flink and Spark both do operator combining automatically, but Storm (without Trident) does not. What this means for Storm is that events go through many more steps and have a higher overhead compared to the other systems.

In addition to further optimizations to Storm, we would like to expand the benchmark in terms of functionality, and to include other stream processing systems like Samza and Apex. We would also like to take into account fault tolerance, processing guarantees, and resource utilization.

The bottom line for us is Storm did great. Writing topologies is simple, and it’s easy to get low latency comparable to or better than Flink up to fairly high throughputs. Without acking, Storm even beat Flink at very high throughput, and we expect that with further optimizations like combining bolts, more intelligent routing of tuples, and improved acking, Storm with acking enabled would compete with Flink at very high throughput too.

The competition between near real time streaming systems is heating up, and there is no clear winner at this point. Each of the platforms studied here have their advantages and disadvantages. Performance is but one factor among others, such as security or integration with tools and libraries. Active communities for these and other big data processing projects continue to innovate and benefit from each other’s advancements. We look forward to expanding this benchmark and testing newer releases of these systems as they come out.

[himanitech]

Wielding Big Data Using PySpark

Introduction to PySpark

PySpark is the Python API for Apache Spark, a distributed computing framework designed to process large-scale data efficiently. It enables parallel data processing across multiple nodes, making it a powerful tool for handling massive datasets.

Why Use PySpark for Big Data?

Scalability: Works across clusters to process petabytes of data.

Speed: Uses in-memory computation to enhance performance.

Flexibility: Supports various data formats and integrates with other big data tools.

Ease of Use: Provides SQL-like querying and DataFrame operations for intuitive data handling.

Setting Up PySpark

To use PySpark, you need to install it and set up a Spark session. Once initialized, Spark allows users to read, process, and analyze large datasets.

Processing Data with PySpark

PySpark can handle different types of data sources such as CSV, JSON, Parquet, and databases. Once data is loaded, users can explore it by checking the schema, summary statistics, and unique values.

Common Data Processing Tasks

Viewing and summarizing datasets.

Handling missing values by dropping or replacing them.

Removing duplicate records.

Filtering, grouping, and sorting data for meaningful insights.

Transforming Data with PySpark

Data can be transformed using SQL-like queries or DataFrame operations. Users can:

Select specific columns for analysis.

Apply conditions to filter out unwanted records.

Group data to find patterns and trends.

Add new calculated columns based on existing data.

Optimizing Performance in PySpark

When working with big data, optimizing performance is crucial. Some strategies include:

Partitioning: Distributing data across multiple partitions for parallel processing.

Caching: Storing intermediate results in memory to speed up repeated computations.

Broadcast Joins: Optimizing joins by broadcasting smaller datasets to all nodes.

Machine Learning with PySpark

PySpark includes MLlib, a machine learning library for big data. It allows users to prepare data, apply machine learning models, and generate predictions. This is useful for tasks such as regression, classification, clustering, and recommendation systems.

Running PySpark on a Cluster

PySpark can run on a single machine or be deployed on a cluster using a distributed computing system like Hadoop YARN. This enables large-scale data processing with improved efficiency.

Conclusion

PySpark provides a powerful platform for handling big data efficiently. With its distributed computing capabilities, it allows users to clean, transform, and analyze large datasets while optimizing performance for scalability.

For Free Tutorials for Programming Languages Visit-https://www.tpointtech.com/

[sandipanks]

https://www.ksolves.com/blog/big-data/apache-spark-kafka-your-big-data-pipeline

Apache Spark and Kafka are two powerful technologies that can be used together to build a robust and scalable big data pipeline. In this blog, we’ll explore how these technologies work together to create a reliable, high-performance data processing solution.

[tutort-academy]

Apache Spark is the most important Hadoop component used in big data and data science. It is an open-source unified analytics engine that promises to handle a variety of tasks quickly and with a standard user interface.

The following features make Apache-Spark one of the most widely used Big Data platforms.

Follow @tutort-academy   for more such information 

[bigdatacourse]

Spark Streaming with HTTP REST endpoint serving JSON data  https://morioh.com/p/68be86644949?f=5c21fb01c16e2556b555ab32 #morioh #structuredstreaming #development #bigdata #apachespark #scala

[yourmanasagudla]

In this video mainly you will learn to Deploy and execute spark submit on @Amazon EMR cluster,   and gets you into Understanding of AWS Elastic Map Reduce with #Spark Example, how to monitor that application ,the stages and executors.EMR stands for Elastic map reduce. you will understand  two technologies one  is cloud computing i.e., #aws and other is  #bigdata .you will get to know how these two technologies will execute our job and give us right performance

[sparkexamples]

Was researching differences between aggregateByKey and reduceByKey and found this answer helpful

[akhila29-blog]

Spark is a data processing engine that provides faster and easy-to-use analytics. It is meant to cover large workloads like batch applications, repetitious algorithms, interactive queries, and streaming. It also reduces the management burden of maintaining separate tools. 

[root2learn-blog]

Top 10 most important Facts of Apache Spark Certification

There can be a great career opportunity for the Apache Spark Certification trainee which is lightning faster.

Apache Spark is mainly developed for the data science purpose to make abstraction easier. Spark can provide you APIs of very high level for Java, Scala, Python and R Programming.

You may get surprised to know that in data processing, Apache Spark is the largest open source project.

Let me Know you some Sparkling Features of Apache Spark :-

1) Dynamic in Nature

We can quickly produce a parallel application, as Spark grants 80 high-level operators.

2)  Reusability

The Apache Spark code can be reused for batch-processing, join stream toward historical data or run ad-hoc queries.

3) Cost Efficient

Apache Spark is a much better cost-effective solution for handling Big data problem because in Hadoop large capacity for storage and the large data center is need during replication.

4) Support for Complicated Analysis

Spark introduced with dedicated tools for a high level of data management which includes streaming data, declarative/interactive queries, machine learning map reduce.

5) Swift Processing

Working with Apache Spark, you can produce a high level of data processing speed which is about 100 times faster in memory and 10 times faster on the disk. This is made feasible by decreasing the number of read-write to disk.

6) Integrated with Hadoop

You will get surprised to know that spark can run independently also and with Hadoop YARN Cluster Manager also and thus it can read existing Hadoop data. which makes Spark flexible.

7) Support Multiple Languages

It is very unique and additional advantage of Apache Spark that it Support for multiple languages like Java, R, Scala, Python. Thus, it provides dynamicity and overcomes the limitation of Hadoop that it can build applications only in Java.

8) Memory Computation in Spark

With in-memory processing, we can improve the data processing speed. Here the data is being stored so there is no need to fetch data from the disk every time which saves your precious time. The reason behind it is Spark has DAG execution engine which helpful in-memory computation and acyclic data flow which results in high speed.

9) Lazy Evaluation in Apache Spark

All the changes which we make in Apache Spark RDD are Idle in nature, that is it does not give the decision at the correct away rather a new RDD is formed from the current one. Consequently, this improves the performance of the system.

Now here comes the most important feature of Apache Spark i.e,.

10) Fault Tolerance in Spark

Apache Spark gives you fault tolerance by Spark abstraction-RDD. Spark RDDs are developed for handling the failure of any working node of the cluster which guarantees the loss of data reduced to zero.

 As per the above facts, you may get clear about the important of Apache Spark Certification and how much it is useful for data handling for the big multinational companies.

[excelworld]

📂 Managed vs. External Tables in Microsoft Fabric

Q: What’s the difference between managed and external tables?

✅ A:

Managed tables: Both the table definition and data files are fully managed by the Spark runtime for the Fabric Lakehouse.

External tables: Only the table definition is managed, while the data itself resides in an external file storage location.

🧠 Use managed tables for simplicity and tight Fabric integration, and external tables when referencing data stored elsewhere (e.g., OneLake, ADLS).

💬 Which one do you use more in your projects—and why?

[govindhtech]

Lightning Engine: A New Era for Apache Spark Speed

Apache Spark analyses enormous data sets for ETL, data science, machine learning, and more. Scaled performance and cost efficiency may be issues. Users often experience resource utilisation, data I/O, and query execution bottlenecks, which slow processing and increase infrastructure costs.

Google Cloud knows these issues well. Lightning Engine (preview), the latest and most powerful Spark engine, unleashes your lakehouse's full potential and provides best-in-class Spark performance.

Lightning Engine?

Lightning Engine prioritises file-system layer and data-access connector optimisations as well as query and execution optimisations.

Lightning Engine enhances Spark query speed by 3.6x on TPC-H workloads at 10TB compared to open source Spark on equivalent equipment.

Lightning Engine's primary advancements are shown above:

Lightning Engine's Spark optimiser is improved by Google's F1 and Procella experience. This advanced optimiser includes adaptive query execution for join removal and exchange reuse, subquery fusion to consolidate scans, advanced inferred filters for semi-join pushdowns, dynamic in-filter generation for effective row-group pruning in Iceberg and Delta tables, optimising Bloom filters based on listing call statistics, and more. Scan and shuffle savings are significant when combined.

Lightning Engine's execution engine boosts performance with a native Apache Gluten and Velox implementation designed for Google's hardware. This uses unified memory management to switch between off-heap and on-heap memory without changing Spark settings. Lightning Engine now supports operators, functions, and Spark data types and can automatically detect when to use the native engine for pushdown results.

Lightning Engine employs columnar shuffle with an optimised serializer-deserializer to decrease shuffle data.

Lightning Engine uses a parquet parser for prefetching, caching, and in-filtering to reduce data scans and metadata operations.

Lightning Engine increases BigQuery and Google Cloud Storage connection to speed up its native engine. An optimised file output committer boosts Spark application performance and reliability, while the upgraded Cloud Storage connection reduces metadata operations to save money. By providing data directly to the engine in Apache Arrow format and eliminating row-to-columnar conversions, the new native BigQuery connection simplifies data delivery.

Lightning Engine works with SQL APIs and Apache Spark DataFrame, so workloads run seamlessly without code changes.

Lightning Engine—why?

Lightning Engine outperforms cloud Spark competitors and is cheaper. Open formats like Apache Iceberg and Delta Lake can boost business efficiency using BigQuery and Google Cloud's cutting-edge AI/ML.

Lightning Engine outperforms DIY Spark implementations, saving you money and letting you focus on your business challenges.

Advantages

Main lightning engine benefits

Faster query performance: Uses a new Spark processing engine with vectorised execution, intelligent caching, and optimised storage I/O.

Leading industry price-performance ratio: Allows customers to manage more data for less money by providing superior performance and cost effectiveness.

Intelligible Lakehouse integration: Integrates with Google Cloud services including BigQuery, Vertex AI, Apache Iceberg, and Delta Lake to provide a single data analytics and AI platform.

Optimised BigQuery and Cloud Storage connections increase data access latency, throughput, and metadata operations.

Flexible deployments: Cluster-based and serverless.

Lightning Engine boosts performance, although the impact depends on workload. It works well for compute-intensive Spark Dataframe API and Spark SQL queries, not I/O-bound tasks.

Spark's Google Cloud future

Google Cloud is excited to apply Google's size, performance, and technical prowess to Apache Spark workloads with the new Lightning Engine data query engine, enabling developers worldwide. It wants to speed it up in the following months, so this is just the start!

Google Cloud Serverless for Apache Spark and Dataproc on Google Compute Engine premium tiers demonstrate Lightning Engine. Both services offer GPU support for faster machine learning and task monitoring for operational efficiency.

[yourmanasagudla]

This Apache drill webinar video will introduce you to the concepts  of Apache drill ,Why to use Apache Spark drill , Drillbit , comparison to other rivals ,an example demo and application (using JSON file) .This Spark Training in Bangalore is ideal for beginners to learn Apache Drill.

[motorcycleaccessories01]

Apache Spark is a fast, scalable, and open-source big data processing engine. It enables real-time analytics, machine learning, and batch processing across large datasets. With in-memory computing and distributed processing, Spark delivers high performance for data-driven applications. Explore Spark’s features and benefits today!

[sparkexamples]

Spark Resource Management and YARN

A concise look at the differences between how Spark and MapReduce manage cluster resources under YARN  http://bit.ly/1DgvzM0 #spark #yarn #apachespark