Big Data Sql
Big Data SQL
Akbar S. Ahmed | Feb 26, 2015

Why are there so many Big Data SQL options?

There are a myriad of SQL tools for big data that all appear to do the same thing. Each tool is developed by a very capable company, such as Facebook’s Hive and Presto, eBay’s Kylin and Pulsar, or Salesforce’s Phoenix.

Trying to pick which Big Data SQL tool is difficult. When reading the website of each tool they all sound the same. Do the engineers at Facebook, eBay, Saleforce and others all suffer from NIH syndrome? So how do you pick which Big Data SQL tool to use?

Fortunately, the engineers that developed each Big Data SQL tool do not suffer from NIH syndrome. Further, each tool is very different from the others with a little overlap in use cases. The large differences between each tool make it easy to pick the right tool for a given project.

What is Big Data SQL?

Big Data SQL is the use of an SQL-like language as the interface to a big data technology.

Big Data SQL has opened the world of Big Data technologies to the large army of database developers who use SQL as their primary language. Further expanding the user base for Big Data is the fact that analysts are also trained on SQL.

Choose the right tool for the job

Before we start to look into individual Big Data SQL tools, we need to discuss the various storage engines that each tool can access. Where your data is stored will limit which tools you can use to query the data.

The following table shows a few popular Big Data storage engines:

Storage engine Description
Cassandra Cassandra, while a full featured database, is also a popular storage engine for other tools.
Flat file Flat file could mean any flat file storage, such as on disk, HDFS, S3, or equivalent.
HBase HBase is a database that runs on top of Hadoop's HDFS.
Hive Hive is not really a storage engine, however it does project structure onto data. Hive's data structures are used by a lot of other Big Data tools.
RDBMS Relational databases are often used for storage.
RDD RDD, or Resilient Distributed Datasets, are used by Spark and can span in-memory or on-disk data.

Big Data SQL

The table below provides a brief overview of each Big Data SQL tool and explains when you would use each tool. As you will see below, each Big Data SQL tool has unique capabilities and little overlap with the other tools.

Tool Storage Description
CQL Cassandra

CQL is Cassandra's native interface. If you use Cassandra, then CQL is a natural choice.

Drill Flat file, HBase, Hive

Drill allows you to run low latency queries on a variety of data sources including both structured and unstructured data.

Choose Drill when your data contains structured and unstructured data or when your schema is rapidly changing.

Hive Flat file, HBase

Hive has two main components: a.) HiveQL, an SQL-like language and b.) Hive as a means of projecting structure on data files.

Internally, HiveQL converts SQL into Map Reduce. Use HiveQL when you need to run MR jobs, but prefer to avoid writing MapReduce.

Impala Flat file (HDFS), HBase

Impala, which was created by Cloudera, has its own MPP engine. Choose Impala when you need to run rapidly returning analytic queries on data stored in Hadoop.

Unlike HiveQL, Impala has its own query engine and does not use MapReduce. Impala is a good option for rapidly returning queries where your data is spread across HBase and HDFS. However, other query engines are a better choice for long running queries.

Kylin Hive, HBase

eBay Kylin is a relatively new entrant to the Big Data SQL world. Kylin is very different from the other Big Data SQL tools.

Kylin adds MOLAP capabilities to Hadoop. If you need to create multi-dimensional cubes on Hadoop data then Kylin is the natural choice. Under the covers, Kylin uses Hive, MapReduce, and HDFS to create cubes, and uses HBase to store cubes.

Phoenix HBase

Phoenix, created by Salesforce, is a client-embedded JDBC driver that converts SQL into native HBase API calls. Phoenix is an excellent choice when you need to run rapidly returning queries on HBase.

Phoenix has some overlap with Impala both target rapidly returning queries on HBase. However, Phoenix is a client-side tool and does not require the installation of a daemon on each data node.

Presto Cassandra, Hive, RDBMS

Facebook Presto provides a unique query capability as it can combine data from multiple storage backends.

Choose Presto when you need to query data that spans multiple storage backends, including a relational database.

Presto uses its own query engine where a Presto worker is installed/runs on each data node.

Pulsar Cassandra, Druid

eBay Pulsar is very different from most of the other Big Data SQL tools. Pulsar is designed to provide an SQL interface for real-time and stream processing of Big Data.

Pulsar is a stream processing solution, so Cassandra and Druid can be used as storage after data has finishing flowing through the stream processing pipeline.

Spark SQL Hive, RDD Spark SQL allows you to use SQL to query RDDs. Choose Spark SQL when you are already using Spark or when you want high performance queries using Spark's RDDs.

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