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The DataOps Blog

Where Change Is Welcome

Visualizing and Analyzing Salesforce Data with Neo4j

By May 16, 2017

Cases in Neo4jGraph databases represent and store data in terms of nodes, edges and properties, allowing quick, easy retrieval of complex hierarchical structures that may be difficult to model in traditional relational databases. Neo4j is an open source graph database widely deployed in the community; in this blog entry I’ll show you how to use StreamSets Data Collector (SDC) to read case data from Salesforce and load it into the graph database using Neo4j’s JDBC driver.

Calling External Libraries from the JavaScript Evaluator

By May 11, 2017

JavaScript logoThe Script Evaluators in StreamSets Data Collector (SDC) allow you to manipulate data in pretty much any way you please. I’ve already written about how you can call external Java code from your scripts – compiled Java code has great performance, but sometimes the code you need isn’t available in a JAR. Today I’ll show you how to call an external JavaScript library from the JavaScript Evaluator.

Quick Tip: Resolving ‘minReplication’ Hadoop FS Error

By May 3, 2017

minReplication ErrorI run StreamSets Data Collector on my MacBook Pro. In fact, I have about a dozen different versions installed – the latest, greatest, older versions, release candidates, and, of course, a development ‘master’ build that I hack on. Preparing for tonight’s St Louis Hadoop User Group Meetup, I downloaded Cloudera’s CDH 5.10 Quickstart VM so I could show our classic ‘Taxi Data Tutorial‘ and Drift Synchronization with Hadoop FS and Apache Hive. Spinning up the tutorial pipeline, I was surprised to see an error: HADOOPFS_13 - Error while writing to HDFS: com.streamsets.pipeline.api.StageException: HADOOPFS_58 - Flush failed on file: '/sdc/taxi/_tmp_sdc-847321ce-0acb-4574-8d2c-ff63529f25b8_0' due to 'org.apache.hadoop.ipc.RemoteException( File /sdc/taxi/_tmp_sdc-847321ce-0acb-4574-8d2c-ff63529f25b8_0 could only be replicated to 0 nodes instead of minReplication (=1). There are 1 datanode(s) running and 1 node(s) are excluded in this operation. I’ll explain what this means, and how to resolve it, in this blog post.

StreamSets Data Collector v2.5 Adds IoT, Spark, Performance and Scale

By April 19, 2017

We’re thrilled to announce version 2.5 of StreamSets Data Collector, a major release which includes important functionality related to the Internet of Things (IoT), high-performance database ingest, integration with Apache Spark and integration into your enterprise infrastructure.  You can download the latest open source release here.

This release has over 22 new features, 95 improvements and 150 bug fixes.

Making Sense of Stream Processing

By April 19, 2017

StreamThere has been an explosion of innovation in open source stream processing over the past few years. Frameworks such as Apache Spark and Apache Storm give developers stream abstractions on which they can develop applications; Apache Beam provides an API abstraction, enabling developers to write code independent of the underlying framework, while tools such as Apache NiFi and StreamSets Data Collector provide a user interface abstraction, allowing data engineers to define data flows from high-level building blocks with little or no coding.

In this article, I’ll propose a framework for organizing stream processing projects, and briefly describe each area. I’ll be focusing on organizing the projects into a conceptual model; there are many articles that compare the streaming frameworks for real-world applications – I list a few at the end.

Drift Synchronization with StreamSets Data Collector and Azure Data Lake

By March 6, 2017

ADLS Drift PipelineOne of the great things about StreamSets Data Collector is that its record-oriented architecture allows great flexibility in creating data pipelines – you can plug together pretty much any combination of origins, processors and destinations to build a data flow. After I wrote the Ingesting Local Data into Azure Data Lake Store tutorial, it occurred to me that the Azure Data Lake Store destination should work with the Hive Metadata processor and Hive Metastore destination to allow me to replicate schema changes from a data source such as a relational database into Apache Hive running on HDInsight. Of course, there is a world of difference between should and does, so I was quite apprehensive as I duplicated the pipeline that I used for the Ingesting Drifting Data into Hive and Impala tutorial and replaced the Hadoop FS destination with the Azure Data Lake Store equivalent.

Read and Write JSON to MapR DB with StreamSets Data Collector

By March 5, 2017

MapR DB logoMapR-DB is an enterprise-grade, high performance, NoSQL database management system. As a multi-model NoSQL database, it supports both JSON document models and wide column data models. MapR-DB stores JSON documents in tables; documents within a table in MapR-DB can have different structures. StreamSets Data Collector enables working with MapR-DB documents with its powerful schema-on-read and ingestion capability.

With StreamSets Data Collector, I’ll show you how easy it is to stream data from MongoDB into a MapR-DB table as well as stream data out of the MapR-DB table into MapR Streams.

Running Scala Code in StreamSets Data Collector

By February 27, 2017

Scala logoThe Spark Evaluator, introduced in StreamSets Data Collector (SDC) version, lets you run an Apache Spark application, termed a Spark Transformer, as part of an SDC pipeline. Back in December, we released a tutorial walking you through the process of building a Transformer in Java. Since then, Maurin Lenglart, of Cuberon Labs, has contributed skeleton code for a Scala Transformer, paving the way for a new tutorial, Creating a StreamSets Spark Transformer in Scala.

Ingest Data into Azure Data Lake Store with StreamSets Data Collector

By February 20, 2017

SDC and Power BIAzure Data Lake Store (ADLS) is Microsoft’s cloud repository for big data analytic workloads, designed to capture data for operational and exploratory analytics. StreamSets Data Collector (SDC) version included an Azure Data Lake Store destination, so you can create pipelines to read data from any supported data source and write it to ADLS.

Since configuring the ADLS destination is a multi-step process; our new tutorial, Ingesting Local Data into Azure Data Lake Store, walks you through the process of adding SDC as an application in Azure Active Directory, creating a Data Lake Store, building a simple data ingest pipeline, and then configuring the ADLS destination with credentials to write to an ADLS directory.

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