Topology submissions can fail due to many reasons:
- JDK is not installed or is not in the Path
- Required java dependencies are not included
- Incompatible java jar dependencies. Example: Storm-eventhub-spouts-9.jar is incompatible with Storm 1.0.1. If you submit a jar with that dependency, topolopgy submission will fail.
- Duplicate names for topologies
/var/log/hdinsight-scpwebapi/hdinsight-scpwebapi.out file on active headnode will contain the error details.
At one point, I was big on Storm and really wanted a .NET client for Storm to take off. Nowadays, I’d rather use Spark Streaming or Kafka Streams for the same kind of streaming data work.
Kafka is a distributed pub-sub messaging system that is popular for ingesting real-time data streams and making them available to downstream consumers in a parallel and fault-tolerant manner. This renders Kafka suitable for building real-time streaming data pipelines that reliably move data between heterogeneous processing systems. Before we dive into the details of Structured Streaming’s Kafka support, let’s recap some basic concepts and terms.
Data in Kafka is organized into topics that are split into partitions for parallelism. Each partition is an ordered, immutable sequence of records, and can be thought of as a structured commit log. Producers append records to the tail of these logs and consumers read the logs at their own pace. Multiple consumers can subscribe to a topic and receive incoming records as they arrive. As new records arrive to a partition in a Kafka topic, they are assigned a sequential id number called the offset. A Kafka cluster retains all published records—whether or not they have been consumed—for a configurable retention period, after which they are marked for deletion.
Read the whole thing.
Sometimes, you need to backup your current running flow, let that flow run at a later date, or make a backup of what is in-process now. You want this in a permanent storage and want to reconstitute it later like orange juice and add it back into the flow or restart it.
This could be due to failures, for integration testing, for testing new versions of components, as a checkpoint, or for many other purposes. You don’t always want to reprocess the original source or files (they may be gone).
Read on for an explanation of how FlowFile streams can do this.
The Azure Data Lake (ADL) vision from the beginning has been to transform business data into intelligence by providing analytics on any data at cloud scale. ADL enterprise customers gain insights on their business data using a wide range of tools and platforms. Today’s release of Cloudera Enterprise 5.11 brings another very valuable and widely-used Hadoop computation platform to the set of platforms that can leverage ADLS. No matter what big data analytics platform you choose, Azure Data Lake Store provides a single high throughput enterprise-scale hierarchical file system data lake repository for big data.
Anyone with an Azure subscription can now deploy Cloudera clusters with ADLS. To get started, you can use the Cloudera Enterprise Data Hub template or the Cloudera Director template on Azure Marketplace to create a Cloudera cluster. Once the cluster is up, see here for more information on how to set up your Cloudera cluster with ADLS today!
That’s an interesting development.
It’s live weather reporting using HDF, Kafka, and Solr.
Here are the environment requirements for implementing:
- HDF (for HDF 2.0, you need Java 1.8).
Now let’s get on to the steps!
There are a lot of moving parts there, but the pieces do plug in well enough and there are a lot of screen shots to guide you along the way.
Within a Data Lake, zones allow the logical and/or physical separation of data that keeps the environment secure, organized, and Agile. Typically, the use of 3 or 4 zones is encouraged, but fewer or more may be leveraged. A generic 4-zone system might include the following:
- Transient Zone — Used to hold ephemeral data, such as temporary copies, streaming spools, or other short-lived data before being ingested.
- Raw Zone – The zone in which raw data will be maintained. This is also the zone where sensitive data must be encrypted, tokenized, or otherwise secured.
- Trusted Zone – After Data Quality, Validation, or other processing is performed on data in the Raw Zone, it becomes the “source of truth” in this zone for downstream systems.
- Refined Zone – Manipulated and enriched data is kept in this zone. This is used to store the output from tools like Hive or external tools that will write into to the Data Lake.
Your particular situation may differ but I’d consider this to be good advice no matter where or how you’re storing data, such as a classical data warehouse or an ODS.
What does your pipeline look like, and what steps are involved?
Some of the file formats were optimized to work in certain situations. For example, Sequence files were designed to easily share data between Map Reduce (MR) jobs, so if your pipeline involves MR jobs then Sequence files make an excellent option. In the same vein, columnar data formats such as Parquet and ORC were designed to optimize query times; if the final stage of your pipeline needs to be optimized, using a columnar file format will increase speed while querying data.
At first, I’d suggest just using delimited files, as it’s easiest that way. Once you have developed a bit of Hadoop maturity, then it makes sense to think about whether rowstore formats (like Parquet and Avro) or columnstore formats (like ORC) make sense for a particular data set.
Classify text using BigDL
In this tutorial, we demonstrate how to solve a text classification problem based on the example found here. This example uses a convolutional neural network to classify posts in the 20 Newsgroup dataset into 20 categories.
We’ve provided a companion Jupyter notebook example on GitHub that you can open in the Jupyter dashboard to execute the code sections.
There’s a lot to this tutorial.
Our ad server publishes billions of messages per day to Kafka. We soon realized that writing a proprietary Kafka consumer able to handle that amount of data with the desired offset management logic would be non-trivial, especially when requiring exactly-once-delivery semantics. We found that the Kafka Connect API paired with the HDFS connector developed by Confluent would be perfect for our use case.
We’ve also found it painful not having a central authority on data structures that can share their respective schemas across all services and applications. Without a central registry for message schemas, data serialization and deserialization for a variety of applications are troublesome and the pipeline is fragile when schema evolution happens. We found Schema Registry is a great solution for this problem.
To address the above two problems, we integrated the Kafka Connect API and Schema Registry into our Kafka-centered data pipeline.
Well worth reading, especially the difficulties that they’ve had during maintenance periods and in lower environments.
H2O Flow is an interactive web-based computational user interface where you can combine code execution, text, mathematics, plots and rich media into a single document, much like Jupyter Notebooks. With H2O Flow, you can capture, rerun, annotate, present, and share your workflow. H2O Flow allows you to use H2O interactively to import files, build models, and iteratively improve them. Based on your models, you can make predictions and add rich text to create vignettes of your work – all within Flow’s browser-based environment. In this blog, we will only focus on its visualization part.
H2O FLOW web service lives in the Spark driver and is routed through the HDInsight gateway, so it can only be accessed when the spark application/Notebook is running
You can click the available link in the Jupyter Notebook, or you can directly access this URL:
Setup is pretty easy.