Kafka consumer is what we use quite often to read data from Kafka. Here, we use this article to explain some key concepts and topics regarding to consumer architecture in Kafka. Consumer Groups We can always group consumers into a consumer group by use case or function of the group. One consumer group might be responsible for delivering records to high-speed, in-memory microservices while another consumer group is streaming those same records to Hadoop.
Overview NoSQL (NoSQL = Not Only SQL) means “not just SQL”. Modern computing systems generate a huge amount of data every day on the network. A large part of these data are handled by relational database management systems (RDBMSs). Its matured relational theory foundation makes data modeling and application programming easier. However, with the wave of informationization and the rise of the Internet, traditional RDBMSs have started to experience problems in some paticular domain.
Overview The latest HDP 2.6.x has both Hive version 1 and 2 installed together. However, it does not allow user to run hive version to command directly, but only use beeline. The lab_dev repository here provides an demo virtual box image to have both Hive version configured properly. Conf. Changes The trick thing to make both hive version working is do not add any setting in the .profile anymore. See below, I comments out all pervious hive settings.
We use this place to collect commonly used HBase shell command for reference. HBase shell is an HBase extensible jruby-based (JIRB) shell to execute some commands(each command represents one functionality) in HBase. HBase shell commands are mainly categorized into 6 parts as follows. Will keep adding more examples here. 1. General Information status Show cluster status. Can be ‘summary’, ‘simple’, or ‘detailed’. The default is ‘summary’. hbase> status hbase> status 'simple' hbase> status 'summary' hbase> status 'detailed' version Output this HBase version.
Background It would be difficult and practically impossible to classify a web page, a document, an email or any other lengthy text notes manually. This is where Naïve Bayes Classifier machine learning algorithm comes to the rescue. A classifier is a function that allocates a population’s element value from one of the available categories. For instance, Spam Filtering is a popular application of Naïve Bayes algorithm. Spam filter here, is a classifier that assigns a label Spam or Not Spam to all the emails.
Background Machine learning is a field of computer science that gives computer systems the ability to “learn” with data, without being explicitly programmed. Machine learning can be broken down into three broad categories: Recommender, Classification, Clustering. Recommender—Recommender systems suggest items based on past behavior or interest. These items can be other users in a social network, or products and services in retail websites. There are some algorithm like Pearson correlation and euclidean distance.
Most of time, we need to find the max or min value of particular columns as well as other columns. For example, we have following employee table. DESC employee; +---------------+------------------------------+----------+--+ | col_name | data_type | comment | +---------------+------------------------------+----------+--+ | name | string | | | work_place | array<string> | | | gender_age | struct<gender:string,age:int>| | | skills_score | map<string,int> | | | depart_title | map<string,array<string>> | | +---------------+------------------------------+----------+--+ 5 rows selected (0.
Learning Spark SQL Level Ent. Level Mid. Level Adv. Published in Sep. 2017. Start reading it. Learning Apache Flink Level Ent. Level Mid. There are very few books about Apache Flink. Besides offical document, this is a good one for people who wants to know Flink quicker. This book, published in the earlier of 2017, covers most of core topics for Flink with examples.
Summary Before christmas, DataFibers has completed the winter release, which has more than 40+ changes requests applied. In this release, DataFibers is featured with first demo combined both data landing and transforming in real time with new web interface. In addition, the preview version of batch processing (by spark) is ready. Details Below is the list of key changes in this release. New Web admin UI released using ReactJs based AOR.
Introduction It is quite often that we need a unique identifier for each single rows in the Apache Hive tables. This is quite useful when you need such columns as surrogate keys in data warehouse, as the primary key for data or use as system nature keys. There are following ways of doing that in Hive. ROW_NUMBER() Hive have a couple of internal functions to achieve this. ROW_NUMBER function, which can generate row number for each partition of data.
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