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  1. Stackups
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  5. Apache Spark vs Trifacta

Apache Spark vs Trifacta

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Trifacta
Trifacta
Stacks19
Followers41
Votes0

Apache Spark vs Trifacta: What are the differences?

<Apache Spark and Trifacta are two powerful tools used in data processing and analysis. Apache Spark is a distributed computing framework, while Trifacta is a data preparation platform. Here are the key differences between Apache Spark and Trifacta.>

  1. Processing Paradigm: Apache Spark is designed for large-scale data processing using the concept of Resilient Distributed Datasets (RDDs) and in-memory computing, making it suitable for real-time processing and iterative algorithms. On the other hand, Trifacta focuses on data preparation tasks like cleaning, structuring, and enriching data to make it suitable for analysis, making it ideal for data wrangling and data cleaning processes.

  2. User Interaction: Apache Spark provides APIs in various languages like Scala, Java, and Python for performing data processing tasks programmatically. In contrast, Trifacta offers a visual interface that allows users to interact with the data through a user-friendly GUI without the need for coding, making it more accessible to non-technical users.

  3. Scalability: Apache Spark is known for its ability to scale up and handle large volumes of data efficiently across a distributed cluster of machines, enabling parallel processing. Trifacta, while capable of processing significant amounts of data, may not offer the same level of scalability as Apache Spark due to its focus on data preparation rather than computation on distributed clusters.

  4. Data Sources: Apache Spark supports various data sources and file formats for input and output, including HDFS, Amazon S3, and Apache Hive, enabling seamless integration with different data storage systems. Trifacta, on the other hand, focuses on working with structured and semi-structured data from sources like databases, CSV files, and Excel spreadsheets, emphasizing data preparation for analytics rather than data ingestion from diverse sources.

  5. Data Transformation Capabilities: Apache Spark provides a wide range of transformation and processing functions through its APIs, allowing users to manipulate data, run complex algorithms, and perform analytics tasks effectively. Trifacta specializes in providing diverse data transformation functionalities, such as data cleaning, normalization, and data structuring, with intuitive visual tools to enhance data quality and consistency before analysis.

  6. Integration with Ecosystem: Apache Spark is part of the Hadoop ecosystem and integrates seamlessly with other Apache projects like Hadoop, Hive, and HBase, facilitating a unified big data processing environment. In comparison, Trifacta can integrate with various data storage platforms, BI tools, and data analytics solutions to streamline the data preparation workflow and enhance interoperability within the data ecosystem.

In Summary, Apache Spark excels in distributed data processing for real-time and iterative tasks, while Trifacta specializes in data preparation and wrangling through a visual interface, catering to different aspects of the data processing pipeline.

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Advice on Apache Spark, Trifacta

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Spark
Apache Spark
Trifacta
Trifacta

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.

It is an Intelligent Platform that Interoperates with Your Data Investments. It sits between the data storage and processing environments and the visualization, statistical or machine learning tools used downstream

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Interactive Exploration; Automated visual representations of data based upon its content in the most compelling visual profile; Predictive Transformation; Intelligent Execution; Collaborative Data Governance.
Statistics
GitHub Stars
42.2K
GitHub Stars
-
GitHub Forks
28.9K
GitHub Forks
-
Stacks
3.1K
Stacks
19
Followers
3.5K
Followers
41
Votes
140
Votes
0
Pros & Cons
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed
No community feedback yet
Integrations
No integrations available
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline
Tableau
Tableau

What are some alternatives to Apache Spark, Trifacta?

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

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.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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