Need advice about which tool to choose?Ask the StackShare community!

Apache Drill

71
170
+ 1
16
Pig

59
111
+ 1
5
Add tool

Apache Drill vs Pig: What are the differences?

Apache Drill vs Pig

Apache Drill and Pig are both data processing tools that are widely used in the big data ecosystem. However, there are several key differences between the two.

  1. Query Language: Apache Drill uses SQL-like queries to interact with data sources, making it easier for users familiar with SQL to work with. On the other hand, Pig uses its own scripting language called Pig Latin, which is designed for expressing data transformations.

  2. Data Formats: Apache Drill natively supports a wide range of data formats, including JSON, Parquet, CSV, Avro, and more. It can directly query these formats without any pre-processing. Whereas, Pig requires data to be transformed into its own format called Pig Storage, which can be a time-consuming process.

  3. Data Processing: Apache Drill is designed to work with both structured and semi-structured data, making it suitable for complex data processing tasks. Pig, on the other hand, is primarily focused on structured data processing and lacks advanced features for handling semi-structured or nested data.

  4. Data Source Connectivity: Apache Drill can connect to various data sources, including Hadoop Distributed File System (HDFS), relational databases, NoSQL databases, and more. Pig, on the other hand, primarily operates on data stored in HDFS or HBase and requires data to be loaded into these systems prior to processing.

  5. Performance: Apache Drill is designed for interactive queries and can provide near real-time results on large datasets. It optimizes query execution using distributed processing, vectorized processing, and columnar storage. Pig, on the other hand, is optimized for batch processing and may not provide the same level of performance for interactive queries.

  6. User Community: Apache Drill has a rapidly growing community of users and contributors, with active development and regular updates. Pig, on the other hand, has been around for longer and has a more established user community, but its development and updates have slowed down in recent years.

In Summary, Apache Drill and Pig differ in terms of query language, data formats, data processing capabilities, data source connectivity, performance, and user community.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Drill
Pros of Pig
  • 4
    NoSQL and Hadoop
  • 3
    Free
  • 3
    Lightning speed and simplicity in face of data jungle
  • 2
    Well documented for fast install
  • 1
    SQL interface to multiple datasources
  • 1
    Nested Data support
  • 1
    Read Structured and unstructured data
  • 1
    V1.10 released - https://drill.apache.org/
  • 2
    Finer-grained control on parallelization
  • 1
    Proven at Petabyte scale
  • 1
    Open-source
  • 1
    Join optimizations for highly skewed data

Sign up to add or upvote prosMake informed product decisions

- No public GitHub repository available -

What is Apache Drill?

Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.

What is Pig?

Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Apache Drill?
What companies use Pig?
See which teams inside your own company are using Apache Drill or Pig.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Apache Drill?
What tools integrate with Pig?
What are some alternatives to Apache Drill and Pig?
Presto
Distributed SQL Query Engine for Big Data
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Apache Calcite
It is an open source framework for building databases and data management systems. It includes a SQL parser, an API for building expressions in relational algebra, and a query planning engine
Apache Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
See all alternatives