Hive as a Service
Hive as a Service introduces a SQL (Structured Query Language) interface to Big Data without the complexity of Hadoop and cloud computing, or the cost usually associated with Hadoop. The service is designed for both data professionals, e.g. data engineers and data scientists, as well as data consumers like business analysts. The latter are regularly highly skilled with SQL but not trained to use Big Data architectures like Hadoop. Hive as a Service reduces the data access, exploration, and analytics to SQL to access, define and manipulate data without the need to understand the underlying architecture.
Hive as a Service vs. Traditional RDBMS
Hive as a Service is different from traditional RDBMS (Relational Database Management System) because it incorporates transparently – independent from user know-how – the benefits of Hadoop and cloud computing, i.e. it auto-scales inexpensively horizontally on demand and offers a pay as you go billing. Data users can concentrate on working with the data and not on technology while enjoying the benefits of combining multiple data sources of any size and a balance between cost and performance. Businesses do not need to hire scarce talents and invest in uncertain technologically complex projects anymore to access Big Data with Hadoop.
What is Apache Hive
Apache Hive is a data warehouse system for Hadoop. Hive’s original goal is to provide a SQL-like interface to data stored in HDFS (Hadoop Distributed File System). Hive’s variant of SQL is called HiveQL (Hive Query Language) and it covers most of the common SQL standards plus some Hive specific extensions. The benefit of Hive is the ability to easily ETL (Extract, Transform, and Load) large datasets stored in Hadoop without the need to write elaborate map-reduce programs. This in turn means that any SQL savvy user can access, interrogate, and manipulate the data stored in HDFS.
Hive permits splitting the metadata (the table definition and data format) and querying from the data management. These table definitions are called external tables and defined as such by using a CREATE EXTERNAL TABLE statement. Data can be added and removed on a file system level by any process, e.g. a process regularly adding log files to a directory. This makes Hive a great choice to integrate data of any size from multiple sources.
Hive, at query time, will make a best effort to read the data available in the table’s location at the time. The usual SELECT and JOIN data retrieval queries are available. Note that a Hive user can still chose to leave the data management to Hive, i.e. create a table, insert into it, or drop it with simple SQL statements as the user may do with traditional systems.
Hive initially did not support indexing and embraced Hadoop’s parallel
data processing speed. Nowadays indexes are available but not widely
used. Hive data access optimisation usually focuses on compressing data
on disk, partitioning, and data file formats. It supports popular
compression algorithms transparently, i.e. writes, read, compresses and
decompresses data automatically based on settings and file endings.
Another common optimisation, especially for time series data, is the
ability to partition data by a hierarchical directory structure, e.g.
dates are common with schemas like
Hive, given a partitioned table definition, optimises queries to access
only the data in question, e.g. to read only a month or day of data
depending on the query. Further developments have culminated in
specialised file formats like ORC, which is easy to use (merely requires
STORED AS ORC in a create table statement) and combine faster data
access with better compression characteristics.
Today Hadoop and Hive also support Amazon Web Services S3 file system besides HDFS. It provides a virtually unlimited durable data sink easily accessible by other systems to store and exchange data. It decoupled the use of Hive from a permanent Hadoop cluster and made Hive as a Service a viable and reliable solution.
Hive is an open source project under the Apache 2.0 license, which combined with its extendable structure, makes it versatile. One aspect is its ability to be extended easily for file formats with a pluggable SerDe (Serializer/Deserializer). It comes with support for Avro, ORC, Regular Expressions, Thrift, and there are open source ones for JSON. Extending to custom legacy or additional common formats is a simple task.
Qubole demonstrates the flexibility of Hive by providing a MongoDB SerDe, which allows users to define tables in Hive relating to collections in MongoDB making importing and exporting data as simple as writing SQL. One of Hive’s strength and reasons for success has been its data agnostic approach. It lends itself to combine data from various inputs, e.g. RDBMS, NoSQL, and data sinks. Hive subsequently has become essential in many data pipelines rivalling functionalities of expensive data warehouse solutions. This popularity has been at the core of the increase of the custom SQL on Hadoop solutions in the market. However, none has the same broad user base and maturity Hive has.
Another reason for Hive’s success is its close integration with Hadoop. It relies on Hadoop, a popular battle proven platform, for processing queries. A HiveQL statement is translated automatically into a series of optimised map-reduce jobs executed with Hadoop. Consequently, Hive’s processing power is directly linked to Hadoop and it can be employed to process a wide variety of data of any size. This is no coincident. Hive has been developed at Facebook by the founders of Qubole to process Petabytes of data. It has been so successful that it is now used by thousands of users at Facebook most of who have never written a map-reduce program.
Why Hive as a Service?
Until recently, the simplicity of Hive to ETL a variety and large volume of data came at the expense of running a complex Hadoop cluster. In large-scale environments the operation of a Hadoop cluster is efficient and inexpensive to comparable solutions. Yet, for many situations a permanent cluster is not cost-efficient or the know-how not available; hiring an experience Hadoop engineer is difficult and technical support for Hadoop distributions is expensive.
There are a limited number of solutions in the market providing Hadoop as a Service to address this. Qubole has gone a step further by providing a fully auto-scaling, on demand, pay as you go Hadoop as a Service and extended it significantly to add Hive as a Service on top of it. Effectively a data scientist or engineer can use the same service as a business user but in different ways.
QDS (Qubole Data Service) supports professional requirements for Hadoop as a Service with complex data pipelines including a wide range of data(base) adapters and scheduled workflows. Latter of which can consist of traditional map-reduce jobs, Pig Latin scripts, or Hadoop streaming. Beyond this sophisticated Big Data solution QDS provides an easy to use graphical web interface to Hive. It reduces data analysis to simple SQL querying and provides wizards to help integrating data sources as well as tutorials and webinars to get started.
Many users have an existing toolset like Microsoft Excel or Tableau, and are a very skilled with it. These users can use Qubole’s ODBC driver to integrate with Hive. This incredibly easy to use integration makes Hadoop processing power available to any business user in a company without the overhead of owning a cluster.
What Qubole does for you
Qubole has brought together the founders behind Apache Hive and a team of excellent Hadoop engineers to provide you with a cloud based on demand Hadoop cluster. You can start using Hive and Hadoop for 15 days as soon as you sign up with Qubole without the need for a credit card. A cluster is started for you as soon as you need it. For example, when you query data with Hive Qubole will detect the processing power required and start an appropriate cluster made up of virtual cloud computing instances for you and pass your query on to it. The progress of the query and the subsequent result of the query will appear on the web interface where you entered the query. Once you stop using the cluster you do not have turn it off. Qubole will detect an idle cluster and shut it down for you.
In the same fashion Qubole will detect changes in utilisation on a cluster and grow or shrink it to ensure you do not waste your money. You will only pay for the time and size of cluster you actually used and you can define the bounds of the cluster size and desired compute instance types to avoid any surprises.
Importantly, all of this intelligent starting, stopping, and scaling of clusters is done transparently in the background without the need of any action by you. The web interface will retain any history of queries, table definitions, and logs. You can pick up your work at any time.
You can try Qubolefor 15 days for free including 300GB free storage on Amazon S3.