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Data Storage Formats for Big Data Analytics: Performance and Cost Implications of Parquet, Avro, and ORC

Sichere Programmierung dzone.com 09.09.2024, 14:00 Uhr 1 Min. Lesezeit KRITIS / Energie Cloud & IT-Enterprise

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Efficient data processing is crucial for businesses and organizations that rely on big data analytics to make informed decisions. One key factor that significantly affects the performance of data processing is the storage format of the data. This article explores the impact of different storage formats, specifically Parquet, Avro, and ORC on query performance and costs in big data environments on Google Cloud Platform (GCP). This article provides benchmarks, discusses cost implications, and offers recommendations on selecting the appropriate format based on specific use cases.

Introduction to Storage Formats in Big Data

Data storage formats are the backbone of any big data processing environment. They define how data is stored, read, and written directly impacting storage efficiency, query performance, and data retrieval speeds. In the big data ecosystem, columnar formats like Parquet and ORC and row-based formats like Avro are widely used due to their optimized performance for specific types of queries and processing tasks.

CTI Threat Relationship Graph2 Knoten / 1 Relationen

CVE / Incident Software MITRE ATT&CK CWE Weakness IoC

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Syntax validiert (0 Fehler)

title: Detect Exploitation - Data Storage Formats for Big Data Analytics: Performance and Cost Implications of Parquet, Avro, and ORC
id: 16bbdf5f-9f1e-47a6-af68-2e841050ba12
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

Syntax validiert (0 Fehler)

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "Data Storage Formats for Big D" ascii wide
    condition:
        any of them
}

Syntax validiert (0 Fehler)

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Data Storage Formats for Big Data Analyt")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count

Syntax validiert (0 Fehler)

message: "*Data Storage Formats for Big Data Analyt*"

Syntax validiert (0 Fehler)

CommonSecurityLog
| where Message has "Data Storage Formats for Big Data Analyt"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

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Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich Data Storage Formats for Big Data Analyt.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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Format: python

#!/usr/bin/env python3
# Exploit Title: PoC for Target
import socket, sys
target = '192[.]168[.]1[.]10'
url = 'hxxp://target[.]local/vuln'
s = socket.socket()
s.connect((target, 80))
s.send(b'GET / HTTP/1.1\r\nHost: target.local\r\n\r\n')