Deep Dive into SnapLogic Winter 2017 Snaps Release

By Pavan Venkatesh

Data streams with Confluent and migration to Hadoop: In my previous blog post, I explained how future data movement trends will look. In this post, I’ll dig into some of the exciting things we announced as part of the Winter 2017 (4.8) Snaps release. This will also address future data movement trends for customers who want to move data to the cloud from different systems or migrate to Hadoop.

Major highlights in 2017 Winter release (4.8) include:

  • Support of Confluent Kafka – A distributed messaging system for streaming data
  • Teradata to Hadoop – A quick and easy way to migrate data
  • Enhancements to the Teradata Snap Pack: On the TPT front, customers can quickly load/update/delete data in Teradata
  • The RedShift Multi-Execute Snap – Allows multiple statements to be sequentially executed, so customers can maintain business logic
  • Enhancements to the MongoDB Snap pack (Delete and Update) and the DynamoDB Snap pack (Delete and Delete-item)
  • Workday Read output enhancements – Now it’s easier for the downstream systems to consume
  • Netsuite Snap Pack improvements -Users can now submit asynchronous operations
  • Security feature enhancements – Including SSL for MongoDB Snap Pack and invalidating database connection pools when account properties are modified
  • Major performance improvement while writing to an S3 bucket using S3 File Writer – Users can now configure a buffer size in the Snap so larger blocks are sent to S3 quickly

Confluent Kafka Snap Pack

Kafka is a distributed messaging system based on publish/subscribe model with high throughput and scalability. It is mainly used for ingestion from multiple sources and then sent to multiple downstream systems. Use cases include website activity tracking, fraud analytics, log aggregation, sales analytics, and others. Confluent is the company that provides the enterprise capability and offering for open source Kafka.

Here at SnapLogic we have built Kafka Producer and Consumer Snaps as part of the Confluent Snap Pack. A deep dive into Kafka architecture and its working will be a good segue before going into the Snap Pack or pipeline details.

kafka-cluster

Kafka consists of single or multiple Producers that can produce messages from a single or multiple upstream systems, and single or multiple Consumers that consume messages as part of downstream systems. A Kafka cluster constitutes one or more servers called Brokers. Messages (key and value or just the value) will be fed into higher level abstraction called Topics. Each Topic can have multiple messages from different Producers. User can also define different Topics for new category of messages. These Producers write messages to Topics and Consumers consume from one or more Topics. Also Topics are partitioned, replicated, and persisted across Brokers. Messages in the Topics are ordered within a partition and each of these will have a sequential ID number called offset. Zookeeper usually maintains these offsets but Confluent calls it coordination kernel.

Kafka also allows configuring a Consumer group where multiple Consumers are part of it, when consuming from a Topic.

With over 400 Snaps supporting various on-prem (relational databases, files, nosql databases, and others) and cloud products (Netsuite, SalesForce, Workday, RedShift, Anaplan, and others), the Snaplogic Elastic Integration Cloud in combination with the Confluent Kafka Snap Pack will be a powerful combination for moving data to different systems in a fast and streaming manner. Customers can realize benefits and generate business outcomes in a quick manner.

With respect to the Confluent Kafka Snap Pack, we support Confluent Version 3.0.1 (Kafka v0.9). These Snaps abstract the complexities and users only have to provide configuration details to build a pipeline which moves data easily. One thing to note is that when multiple Consumer Snaps are used in a pipeline and have been configured with the same consumer group, then each Consumer Snap will be assigned a different subset of partitions in the Topic.

kafka-producer

kafka-consumer

pipeline1

In the above example, I built a pipeline where sales leads (messages) stored in local files and MySQL are sent to a Topic in Confluent Kafka via Confluent Kafka Producer Snaps. The downstream system Redshift will consume these messages from that Topic via the Confluent Kafka Consumer Snap and bulk load it to RedShift for historical or auditing needs. These messages are also sent to Tableau as another Consumer to run analytics on how many leads were generated this year, so customer can compare this against last year.

Easy migrations from Teradata to Hadoop

There has been a major shift where customers are moving from expensive Teradata solutions to Hadoop or other data warehouse. Until now, there has not been an easy solution in transferring large amounts of data from Teradata to big data Hadoop. With this release we have developed a Teradata Export to HDFS Snap with two goals in mind: 1) ease of use and 2) high performance. This Snap uses the Teradata Connector for Hadoop (TDCH v1.5.1). Customers just have to download this connector from the Teradata website in addition to the regular jdbc jars. No installation required on either Teradata or Hadoop nodes.

TDCH utilizes MapReduce (MR) as its execution engine where the queries gets submitted to this framework, and the distributed processes launched by the MapReduce framework make JDBC connections to the Teradata database. The data fetched will be directly loaded into the defined HDFS location. The degree of parallelism for these TDCH jobs is defined by the number of mappers (a Snap configuration) used by the MapReduce job. The number of mappers also defines the number of files created in HDFS location.

The Snap account details with a sample query to extract data from Teradata and load it to HDFS is shown below.

edit-account

terradata-export

 

The pipeline to this effect is as follows:

pipeline2

As you can see above, you use just one Snap to export data from Teradata and load it into HDFS. Customers can later use HDFS Reader Snap to read files that are exported.

Winter 2017 release has equipped customers with lots of benefits, from data streams, easy migrations, to enhancing security functionality, and performance benefits. More information on the SnapLogic Winter 2017 (4.8) release can be found in the release notes.

Pavan Venkatesh is Senior Product Manager at SnapLogic. Follow him on Twitter @pavankv.

7 Big Data Predictions for 2017

As data increasingly becomes the means by which businesses compete, companies are restructuring operations to build systems and processes liberating data access, integration and analysis up and down the value chain. Effective data management has become so important that the position of Chief Data Officer is projected to become a standard senior board level role by 2020, with 92 percent of CIOs stating that a CDO is the best person to determine data strategy.

With this in mind as you evaluate your data strategy for 2017, here are seven predictions to contemplate to build a solid framework for data management and optimization.

  1.  Self-Service Data Integration Will Take Off
    Eschewing the IT bottleneck designation and committed to being a strategic partner to the business, IT is transforming its mindset. Rather than be providers of data, IT will enable users to achieve data optimization on a self-service basis. IT will increasingly decentralize app and data integration – via distributed Centers of Excellence based on shared infrastructure, frameworks and best practices – thereby enabling line-of-business heads to gather, integrate and analyze data themselves to discern and quickly act upon insightful trends and patterns of import to their roles and responsibilities. Rather than fish for your data, IT will teach you how to bait the hook. The payoff for IT: satisfying business user demand for fast and easy integrations and accelerated time to value; preserving data integrity, security and governance on a common infrastructure across the enterprise; and freeing up finite IT resources to focus on other strategic initiatives.
  1. Big Data Moves to the Cloud
    As the year takes shape, expect more enterprises to migrate storage and analysis of their big data from traditional on-premise data stores and warehouses to the cloud. For the better part of the last decade, Hadoop’s distributed computing and processing power has made it the standard open source platform for big data infrastructures. But Hadoop is far from perfect. Common user gripes include complexity and instability – not all that surprising given all the software developers regularly contributing their improvements to the platform. Cloud environments are more stable, flexible, elastic and better-suited to handling big data, hence the predicted migration.
  1. Spark Usage Outside of Hadoop Will Surge
    This is the year we will also see more Spark use cases outside of Hadoop environments. While Hadoop limps along, Spark is picking up the pace. Hadoop is still more likely to be used in testing rather than production environments. But users are finding Spark to be more flexible, adaptable and better suited for certain workloads – machine learning and real-time streaming analytics, as examples. Once relegated to Hadoop sidekick, Spark will break free and stand on its own two feet this year. I’m not alone in asking the question: Hadoop needs Spark but does Spark need Hadoop?
  1. A Big Fish Acquires a Hadoop Distro Vendor?
    Hadoop distribution vendors like Cloudera and Hortonworks paved the way with promising technology and game-changing innovation. But this past year saw growing frustration among customers lamenting increased complexity, instability and, ultimately, too many failed projects that never left the labs. As Hadoop distro vendors work through some growing pains (not to mention limited funds), could it be that a bigger, deeper-pocketed established player – say Teradata, Oracle, Microsoft or IBM – might swoop in to buy their sought after technology and marry it with a more mature organization? I’m not counting it out.
  1. AI and ML Get a Bit More Mainstream
    Off the shelf AI (artificial intelligence) and ML (machine learning) platforms are loved for their simplicity, low barrier to entry and low cost. In 2017, off the shelf AI and ML libraries from Microsoft, Google, Amazon and other vendors will be embedded in enterprise solutions, including mobile varieties. Tasks that have until now been manual and time-consuming will become automated and accelerated, extending into the world of data integration.

6. Yes, IoT is Coming, Just Not This Year
Connecting billions and billions of sensor-embedded devices and objects over the internet is inevitable, but don’t yet swallow all the hype. Yes, there is a lot being done to harness IoT for specific aims, but the pace toward the development of a general-purpose IoT platform is closer to a canter than a gallop. IoT solutions are too bespoke and purpose-built to solve broad, commonplace problems – the market still nascent with standards gradually evolving – that a general-purpose, mass-adopted IoT platform to collect, integrate and report on data in real-time will take, well, more time. Like any other transformation movement in the history of enterprise technology, brilliant bits and pieces need to come together as a whole. It’s coming, just not in 2017.

  1. APIs Are Not All They’re Cracked Up to Be
    APIs have long been the glue connecting apps and services, but customers will continue to question their value vs investment in 2017. Few would dispute that APIs are useful in building apps and, in many cases, may be the right choice in this regard. But in situations where the integration of apps and/or data is needed and sought, there are better ways. Case in point is iPaaS (integration platform as a service), which allows you to quickly and easily connect any combination of cloud and on-premise technologies. Expect greater migration this year toward cloud-based enterprise integration platforms – compared to APIs, iPaaS solutions are more agile, better equipped to handle the vagaries of data, more adaptable to changes, easier to maintain and far more productive.

I could go on and on, if for no other reason that predictions are informed “best guesses” about the future. If I’m wrong on two or three of my expectations, my peers will forgive me. In the rapidly changing world of technology, batting .400 is a pretty good statistic.

Future Data Movement Trends with SnapLogic

Data volumes are exponentially increasing and many organizations are starting to realize the complexity of their growing data movement and data management solutions. Data exists in various systems, and getting meaningful value out of it has become a major challenge for many companies. Also, most of the data is usually stored in relational systems like MySQL, PostgreSQL and Oracle, these being the mainstream databases primarily used for OLTP purposes. NoSQL systems like Cassandra, MongoDB and DynamoDB have also emerged with tunable consistency model in order to store some of these mission critical data. Customers then typically move these data to much bigger systems like Teradata and Hadoop (OLAP) that can store large amounts of data, so they can run analytics, reporting or complex queries against it. There is also a recent trend where some of these data are moved to the cloud, especially to Amazon RedShift or Snowflake and also to HDInsights or Azure Data Warehouse.

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SnapLogic Sits Down with theCUBE at AWS re:Invent to Talk Self-Service Cloud Analytics

SnapLogic was front-and-center at AWS re:Invent last week in Las Vegas, with our team busier than ever meeting with customers and prospects, showcasing our solutions at the booth, and networking into the evening with event-goers interested in all things Cloud, AWS integration and SnapLogic.

Ravi Dharnikota, SnapLogic’s Head of Enterprise Architecture and Big Data Practice, took time out to stop by and visit with John Furrier, co-founder of the live video interview show theCUBE.  Ravi was joined by Matt Glickman, VP of Products with our partner Snowflake Computing, for a wide-ranging discussion on the changing customer requirements for effective data integration, SaaS integration, warehousing and analytics in the cloud.  

The roundtable all agreed — organizations need fast and easy access to all data, no matter the source, format or location — and legacy solutions built for a bygone era simply aren’t cutting it.  Enter SnapLogic and Snowflake, each with a modern solution designed from the ground-up to be cloud-first, self-service, fully scalable and capable of handling all data. Customers using these solutions together — like Kraft Group, owners of the New England Patriots and Gillette Stadium — enjoy dramatic acceleration in time-to-value at a fraction of the cost by eliminating manual configuration, coding and tuning while bringing together diverse data and taking full advantage of the flexibility and scalability of the cloud.

To make it even easier for customers, SnapLogic and Snowflake recently announced tighter technology integration and joint go-to-market programs to help organizations harness all data for new insights, smarter decisions and better business outcomes.

To watch the full video interview on theCUBE, click here.

Making Workday Faster for Vassar College

Last week we attended Workday Rising in Chicago where we talked to attendees about integrating Workday with the rest of their IT ecosystems. The real stars of the show, however, were our customers from Vassar College who gave a brief presentation at our booth to discuss their journey from finding the need for an integration vendor, to assessing different platforms, to ultimately choosing SnapLogic’s elastic integration platform as a service (iPaaS).vassar-college-image-edited

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SnapLogic CTO James Markarian on DisrupTV

SnapLogic CTO James Markarian recently appeared as a guest on DisrupTV, a weekly live-interview web-series produced by analyst firm Constellation Research and hosted by R “Ray” Wang and Vala Afshar. The trio discussed a variety of enterprise topics including modern data management, data lake strategy considerations and big data analytics.

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Planview Selects SnapLogic Platform As Global Integration Standard

Planview, the leading provider of Work and Resource Management (WRM) solutions, has announced that it has standardized on the SnapLogic platform to help their global customers integrate Planview’s WRM solutions with any other application in their ecosystems, whether in the cloud or on-premises.

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