Skip to main content

The Forrester Wave (Or: We're all the leaders)

Listen:
Forrester Research, an independent market research firm, released in February 2014 the quarterly Forrester Wave Big Data Hadoop Solutions, Q1 2014 Report [1]. The report shows this graphic, and it looks like that all major, minor and non-hadoop Vendors think they lead. It looks really funny when you follow the mainstream press news.

IBM [5] think they lead, Hortonworks [4] claim the leadership too, MapR [3] leads too, Teradata is the true leader (they say) [6]. Cloudera [2] ignores the report. The metapher is - all of the named companies are in the leader area, but nobody leads.

Forrester Wave Big Data Hadoop Solutions, Q1 2014 Report
Anyway, let us do a quick overview about the "Big Three" - Cloudera, MapR, Hortonworks.

The 3 major Hadoop firms (Horton, MapR, Cloudera) are nearly in the same position. All distributions have the sweet piece, which lets the customer decide which one fits most. And that is the most important point - the customer wins. Not the marketing noise.

Cloudera [2] depends on Apache Hadoop, has Cloudera Manager, a strong, sophisticated and great tool to manage an entire hadoop cluster, including add, relocate and remove services from a node to another. In addition to the Open Source version of Hadoop they offer Closed Source Applications on top, like Cloudera Manager Enterprise, Cloudera Navigator (Data Lineage), BDR, Snapshotting, Data Replication. But these additional services aren't OpenSource.

MapR [3] is the most convenient guy here - the press release on their website is clear, no big noise. The message: Choose what is the best for your business. Makes the company a bit friendly. MapR has 3 different solutions - M3, the free-to-use edition, M5 - the Enterprise Edition with NFS Support, Snapshotting, independent code support and M7, the Enterprise Database Edition, optimized for Low Latency and High Throughput. MapR Editions aren't Open Source, and the management console is not as feature-rich as Cloudera Manager. Additionally, the company created their own HDFS-like file system (MapR-FS), mostly written in C(++).

Hortonworks [4] is the youngest player in the market. Originally Horton comes from Yahoo and is a spin-off from the core developers on Apache Hadoop MapReduce, Apache Hadoop HDFS and Apache Hadoop Yarn. HDP, the Hortonworks Edition of Apache Hadoop, is the only 100% Open Source distribution in the market. The managing tool, Apache Ambari (incubating) is also not so feature-rich as Cloudera Manager, but it's Open Source and works well. Furthermore, Horton sells only Apache Projects in their distribution, for Data Governance Falcon, and for Security Purposes Knox.

All of  these three players have a strong support department as well as service delivery (Solution Architect), Pre- and Post Sales and a significant amount of customers.

In my eyes, I see only one true leader. Apache Hadoop. All of those "BigData" companies rely on a great idea, originally developed at Google and rebuilt by the Apache Open Source Community. This is what true leadership means - evolve and divide.

[1] http://www.forrester.com/pimages/rws/reprints/document/112461/oid/1-PBE69P
[2] http://www.cloudera.com
[3] http://www.mapr.com/forrester-wave-hadoop-distribution-comparison-and-benchmark-report
[4] http://info.hortonworks.com/ForresterWave_Hadoop.html
[5] http://www.ibmbigdatahub.com/whitepaper/forrester-wave-big-data-hadoop-solutions-q1-2014
[6] http://www.teradata.de/News-Releases/2014/Teradata-is-a-Leader-in-Big-Data-Hadoop-Solutions-in-2014/?LangType=1031 

Comments

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this inform

Hive query shows ERROR "too many counters"

A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s

AI's False Reality: Understanding Hallucination

Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese