Building TensorFlow Neural Networks On Spark With Keras

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library:

Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and respective activation functions. Binary classification is a common machine learning task applied widely to classify images or text into two classes. For example, an image is a cat or dog; or a tweet is positive or negative in sentiment; and whether mail is spam or not spam.

But the point here is not so much to demonstrate a complex neural network model as to show the ease with which you can develop with Keras and TensorFlow, log an MLflow run, and experiment—all within PyCharm on your laptop.

Click through for the video and explanation of the process.

Hortonworks Data Platform 3.0 Released

Saumitra Buragohain, et al, announce the newest version of the Hortonworks Data Platform:

Highlighted Apache Hive features include:

  • Workload management for LLAP:  You can assign resource pools within LLAP pool and allocate resources on a per user or per group basis. This enables support for large multi-tenant deployments.

  • ACID v2 and ACID on by default:  We are releasing ACID v2. With the performance improvements in both storage format and execution engine we are seeing equal or better performance when comparing to non-ACID tables. Thus we are turning ACID on by default and enable full support for data updates.

  • Hive Warehouse Connector for Spark:  Hive Warehouse Connector allows you to connect Spark application with Hive data warehouses. The connector automatically handles ACID tables. This enables data science workloads to work well with data in Hive.

  • Materialized view navigation:  Materialized view allows you to pre-aggregate and pre-compute tables used in queries. Typically works best on sub-queries or intermediate tables. The cost based optimizer will automatically plan a query if those intermediate results are available, drastically speed up your queries.

  • Information schema:  Hive now exposes the metadata of the database (tables, columns etc.) via Hive SQL interface directly.

  • JDBC storage connector:  You can now map any JDBC databases into Hive’s catalog. This means you can join data across Hive and other databases using Hive query engine

This looks pretty good.  So of course I learn about it two days after I rebuild my demo Hadoop cluster.

Replicating Data In HDFS Between Clusters

Murali Ramasami and Niru Anisetti have an article showing how to use the Hortonworks Data Lifecycle Manager to set up replication between two Hadoop clusters:

Data Lifecycle Manager (DLM) delivers on the promise of location-agnostic, secure replication by encapsulating and copying data seamlessly across physical private storage and public cloud environments. This empowers businesses to deliver the right data in the right environment to power the right use cases.

DLM v1.1 provides a complete solution to replicate data, metadata and security policies between on-premises and in cloud. It also supports data movement for data-at-rest and data-in-motion – whether the data is encrypted using a single key or multiple keys on both source and target clusters. DLM supports HDFS and Apache Hive dataset replication.

With DLM infrastructure administrators can manage their data, metadata and security management on-prem and in-cloud using a single-pane of glass that is built on open source technology. Business users can consume their workload outputs in the cloud with data-source-abstraction. DLM also enables business to reduce their capital expenditures and enjoy the benefits of flexibility and elasticity that cloud provides.

Click through for a demo.  May HDFS replication have as long a life and slightly less vitriol than SQL Server replication.

Installing Confluent Platform On Windows

Niels Berglund shows how to install Confluent Platform (the Confluent branded version of Apache Kafka) on a Windows machine using the Windows Subsystem for Linux:

WSL is primarily aimed at developers, and it allows you to run Linux environments directly on Windows in a native format and without the overhead of a virtual machine. Let us retake a look at that statement: run Linux environments directly on Windows in a native format. Yes native format, WSL is not a UNIX-like environment like Cygwin, which wraps non-Windows functionality in Win32 system calls but it serves Linux programs as special, isolated minimal processes (pico-processes) attached to kernel-mode pico-providers. If you want to read all the “gory” details about WSLWindows Subsystem for Linux Overview gives you an excellent introduction.

Surprisingly, it’s pretty easy—I would have expected some strange compatibility issues.

Sharing R Notebooks

Hanyu Cui and Hossein Falaki show how to share a notebook using RMarkdown:

RMarkdown is the dynamic document format RStudio uses. It is normal Markdown plus embedded R (or any other language) code that can be executed to produce outputs, including tables and charts, within the document. Hence, after changing your R code, you can just rerun all code in the RMarkdown file rather than redo the whole run-copy-paste cycle. And an RMarkdown file can be directly exported into multiple formats, including HTML, PDF,  and Word.

Click through for the demo.

Ingesting Google Analytics Data Into Kafka With Python

Bill Ward shows us an example using Python of reading Google Analytics data into a Kafka topic:

Google Analytics is a very powerful platform for monitoring your web site’s metrics including top pages, visitors, bounce rate, etc. As more and more businesses start using Big Data processes, the need to compile as much data as possible becomes more advantageous. The more data you have available, the more options you have to analyze that data and produce some very interesting results that can help you shape your business.

This article assumes that you already have a running Kafka cluster. If you don’t, then please follow my article Kafka Tutorial for Fast Data Architecture to get a cluster up and running. You will also need to have a topic already created for publishing the Google Analytics metrics to. The aforementioned article covers this procedure as well. I created a topic called admintome-ga-pages since we will be collection Google Analytics (ga) metrics about my blog’s pages.

In this article, I will walk you through how to pull metrics data from Google Analytics for your site then take that data and push it to your Kafka Cluster. In a later article, we will cover how to take that data that is consumed in Kafka and analyze it to give us some meaningful business data.

This is a good tutorial and I’m looking forward to part two.

Benefits To Federating The Hadoop NameNode

Hanisha Koneru and Arpit Agarwal show us a few benefits to NameNode federation:

The Apache Hadoop Distributed File System (HDFS) is highly scalable and can support petabyte-sizes clusters.  However, the entire Namespace (file system metadata) is stored in memory. So even though the storage can be scaled horizontally, the namespace can only be scaled vertically. It is limited by the how many files, blocks and directories can be stored in the memory of a single NameNode process.

Federation was introduced in order to scale the name service horizontally by using multiple independent Namenodes/ Namespaces. The Namenodes are independent of each other and there is no communication between them. The Namenodes can share the same Datanodes for storage.

KEY BENEFITS

Scalability: Federation adds support for horizontal scaling of Namespace

Performance: Adding more Namenodes to a cluster increases the aggregate read/write throughput of the cluster

Isolation: Users and applications can be divided between the Namenodes

Read on for examples.

How Qubole Optimizes Apache Spark Clusters

Mikhail Stolpner gives us some tips on how to optimize Apache Spark clusters:

There are four major resources: memory, compute (CPU), disk, and network. Memory and compute are by far the most expensive. Understanding how much compute and memory your application requires is crucial for optimization.

You can configure how much memory and how many CPUs each executor gets. While the number of CPUs for each task is fixed, executor memory is shared between the tasks processed by a single executor.

A few key parameters provide the most impact on how Spark is executed in terms of resources: spark.executor.memoryspark.executor.coresspark.task.cpus, spark.executor.instances, and spark.qubole.max.executors.

This article gives us some idea of the levers we have available as well as when to pull them.  Though the article itself is vendor-specific, a lot of the advice is general.

RStudio Integration With Databricks

Brian Dirking, et al, announce support between RStudio and the Databricks platform:

With Databricks RStudio Integration, both popular R packages for interacting with Apache Spark, SparkR or sparklyr can be used the inside the RStudio IDE on Databricks. When multiple users use a cluster, each creates a separate SparkR Context or sparklyr connection, but they are all talking to a single Databricks managed Spark application allowing unique opportunities for collaboration between users. Together, RStudio can take advantage of Databricks’ cluster management and Apache Spark to perform such as a massive model selection as noted in the figure below.

I like seeing this level of integration, especially from a language like R, which has historically been limited to operating on a single machine’s memory.

Auto-Scaling Amazon ElasticMapReduce

Brandon Schller gives us some tips on sizing and scaling ElasticMapReduce:

EMR scaling is more complex than simply adding or removing nodes from the cluster. One common misconception is that scaling in Amazon EMR works exactly like Amazon EC2 scaling. With EC2 scaling, you can add/remove nodes almost instantly and without worry, but EMR has more complexity to it, especially when scaling a cluster down. This is because important data or jobs could be running on your nodes.

To prevent data loss, Amazon EMR scaling ensures that your node has no running Apache Hadoop tasks or unique data that could be lost before removing your node. It is worth considering this decommissioning delay when resizing your EMR cluster. By understanding and accounting for how this process works, you can avoid issues that have plagued others, such as slow cluster resizes and inefficient automatic scaling policies.

If you’re using EMR today or think you might use it in the future, you should read this.

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