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  5. Apache Parquet vs Talend

Apache Parquet vs Talend

OverviewDecisionsComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Talend
Talend
Stacks297
Followers249
Votes0

Apache Parquet vs Talend: What are the differences?

# Introduction

Apache Parquet and Talend are both widely used technologies in the field of big data processing. However, they have distinct differences that make them suited for different use cases.

1. **File format**: Apache Parquet is a columnar storage file format that is specifically designed for big data processing and analytics. Talend, on the other hand, is an open-source data integration platform that allows users to connect, access, and manage data from various sources.
2. **Use case**: Apache Parquet is ideal for scenarios where efficient reading and writing of large data sets is a priority, making it a popular choice for analytics and data processing tasks. Talend, on the other hand, is more focused on data integration, transformation, and ETL (Extract, Transform, Load) processes, making it suitable for data warehousing and data migration projects.
3. **Scalability**: Apache Parquet is highly scalable and can handle large volumes of data efficiently, making it well-suited for big data applications. Talend, on the other hand, provides scalable data integration capabilities, allowing users to process data from multiple sources and systems seamlessly.
4. **Performance**: Apache Parquet offers high performance due to its columnar storage format, which allows for efficient processing of queries and data retrieval. Talend, on the other hand, focuses on providing a user-friendly interface and comprehensive set of tools for data management, making it easier for users to design and execute data integration workflows.
5. **Community support**: Apache Parquet has a strong community of users and contributors who actively contribute to its development and maintenance. Talend also has a vibrant community that provides support, resources, and plugins for extending its functionality and integrating with other systems.
6. **Flexibility**: Apache Parquet offers flexibility in terms of schema evolution, allowing users to add or remove columns without impacting existing data. Talend provides flexibility in data integration processes, allowing users to create custom workflows and transformations to suit their specific needs.

In Summary, Apache Parquet is optimized for efficient storage and retrieval of large datasets for analytics, while Talend is suited for data integration, ETL processes, and managing data from various sources.

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Advice on Apache Parquet, Talend

karunakaran
karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.5k views80.5k
Comments

Detailed Comparison

Apache Parquet
Apache Parquet
Talend
Talend

It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.

Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
-
Statistics
Stacks
97
Stacks
297
Followers
190
Followers
249
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
No integrations available

What are some alternatives to Apache Parquet, Talend?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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