Skip to main content

The enterprise search pie

The enterprise search market is heating up. From the traditional proprietary software suites to Solr based new entrants, the market landscape is changing. Some of the key trends observed lately include:
-          key spike in the offerings basket;
-          aggressive pricing by traditional software;
-          increased interest in Solr, Lucene based offerings;
-          cloud based setups.

Search and content analytics is a key player in much of enterprise set up, including the following, but not limited to:
-          eDiscovery
-          ERP, CRM suites
-          e-commerce platforms
-          Decision management systems
-          Content management systems

From a Hadoop ecosystem perspective, Cloudera and MapR announced new offerings combined with PR blitz.  The following options are thus available now for the Hadoop enthusiasts:
-          IBM Infosphere Data Explorer –
o       Earlier known as Vivismo and now acquired by IBM, this software brings federated navigation and discovery across a broad range of enterprise content and big data to maximize return on information. Real time and batch indexing of results can be done in conjunction with BigInsights Hadoop distribution
-          Apache Solr with DataStax –
o       Through integration with Apache Cassandra, Solr distribution with DataStax Hadoop gains valuable big data capabilities for enterprise search, including:
§         Continuously available Solr search
§         Real-time search
§         Data durability
§         Seamless integration across mixed workloads
-          Cloudera Search –
o       The recently launched Cloudera Search has Apache Solr integrated with CDH, including Apache Lucene, Apache SolrCloud, Apache Flume, Apache Hadoop MapReduce & HDFS, and Apache Tika. Cloudera Search also includes integrations that make searching more scalable, easy to use, and optimized for both near-real-time and batch-oriented indexing. Cloudera has adapted the SolrCloud project  and leveraged Apache Zookeeper to coordinate distributed processing.
-          LucidWorks search with MapR –
o       With this offering, MapR has now provided querying and data mining capabilities into MapR distibuted file system. Using the connector framework, LucidWorks Search hooks on to “MapR cluster and start ingesting all the data, essentially making that data searchable”.

Similar Solr custom integrations can also be developed with Hortonworks Data Platform, Windows Azure and WanDisco distribution though they may not be as refined as the ones listed above.

From a customer perspective, this is an exciting time as Hadoop distributions venture out in broader territory offering them easier data mining capabilities. As the wider interest and competition picks up, we are expecting the enterprise search offerings to be more commoditized and feature-rich in near future.


Popular posts from this blog

Offloading legacy with Hadoop

With most Fortune 500 organizations having invested in mainframes and other workload systems in the past, the rise of Big Data platforms poses newer integration challenges. The data integration and ETL players are finding fresh opportunities to solve business and IT problems within the Hadoop ecosystem.
To understand the context, challenges and opportunities, we asked a few questions to Syncsort CEO Lonne Jaffe. Syncsort provides fast, secure, enterprise-grade software spanning Big Data in Apache Hadoop to Big Iron on mainframes. At Syncsort, Lonne Jaffe is focusing on accelerating the growth of the company's high-performance Big Data offerings, both organically and through acquisition.
From mainframes to Hadoop and other platforms, Syncsort seems to have been evolving itself continuously. Where do you see Syncsort heading further?Lonne Jaffe: Syncsort is extraordinary in its ability to continuously reinvent itself. Today, we’re innovating around Apache Hadoop and other Big Data pla…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…