Datastreamer Data Pipelines for Unstructured Data Mon, 23 Dec 2024 16:47:51 +0000 en-US hourly 1 https://datastreamer.io/wp-content/uploads/2022/04/cropped-DATASTREAMER-2048x331-1-32x32.png Datastreamer 32 32 Estimating NLP/ML Model Creation Costs https://datastreamer.io/estimating-nlp-ml-model-creation-costs/ Mon, 23 Dec 2024 16:45:12 +0000 https://datastreamer.io/?p=42333 Estimating NLP/ML Model Creation Costs By Tyler Logtenberg Decemeber 2024 | 7 min. read Table of Contents To account for the estimated costs in the creation and managing of an NLP/ML classifier or model, there are three key elements: the human resources required (manpower), the infrastructure costs, and the ongoing maintenance costs to sustain the […]

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Estimating NLP/ML Model Creation Costs

By Tyler Logtenberg

Decemeber 2024 | 7 min. read

Table of Contents

To account for the estimated costs in the creation and managing of an NLP/ML classifier or model, there are three key elements: the human resources required (manpower), the infrastructure costs, and the ongoing maintenance costs to sustain the new capability. 

Estimating Resource Costs

While the complexity of NLP/ML classifier models varies heavily depending on the use cases, this estimation is based on the creation of a semi-complex NLP classifier. An example of this is sentiment extraction or entity detection.

The average effort for the creation of a semi-complex NLP or ML classifier can vary in size, but often can be estimated at a duration of 8 ‘sprints.’ A Sprint is a measurement within engineering teams of dedicated time to specific stories and generally is aligned with 2 week cycles. This brings our estimation of duration to 16 weeks from planning to production release. The usual team composition and costs that are most common seen are laid out below:

ResourceMonthly EstimateCount
Data Scientist$13,3331
Data Engineer$8,8301
ML Ops Engineer$9,1821
Resource Cost$31,3453

Using this estimated 3-month duration of complete effort, the Resource Costs of the NLP/ML Classifier and Model would be $94,035 and does not include other documentation, product marketing, QA, or project management costs. 

Infrastructure Estimated Costs

In addition to the resource costs, there are many supporting costs across infrastructure and supporting teams.

The below estimation is illustrative of many of the regular costs, but does not include the costs in acquiring any training data, nor external API integrations.

InfrastructureMonthly EstimateOngoing
Model Training$50Yes
Inference Costs$1,700*Yes
Model Storage$0.80Yes
MLOps Tools$1,000Yes
Pipeline Setup$5,659**No
Infrastructure Cost$7,357

Using this estimated 3-month duration of dedicated effort, the support costs of the NLP/ML classifier and model would be $10,755. 

*If you are building a simpler solution that relies on data of low dimensionality, you may get by with four virtual CPUs running on one to three nodes. In processing mid to large volumes of web data, this generally would require a GPU-based server (Pricing from GCP).

** An integration of a simple data pipeline and needed APIs to integrate a model into the overall platform system takes up around 100 development hours. This does not account for documentation, QA, and external API integrations. 

Estimated Maintenance Costs & Summary

According to a study conducted by Dimensional Research, businesses commit 25% to 75% of the initial resources to maintaining ML algorithms. As we have assumed the usage of MLOps tooling, and other resources; the lower end of the estimated percentage was used to account for annual costs.

InfrastructureMonthly EstimateCommit %
Human Resources$65325%
Inference Costs$1,700Full
Model Storage$0.80Full
MLOps Tools$1,00025%
Pipeline Setup$9420%
Maintenance Cost$2,698

The total costs summarized for a NLP/ML model are then best separated into the initial project costs and ongoing maintenance.

This brings us to the total estimated costs below, as confirmed by market research by Datastreamer, Dimensional Research, UpsilonIT, and ITRex Group.

NLP/ML Classifiers and Model Creation Costs
Initial Model CreationOngoing Monthly Maintenance
$116,108 USD$2,698 USD

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]]> Estimating the Cost to Add a Web Data Source https://datastreamer.io/estimating-the-cost-to-add-a-web-data-source/ Mon, 23 Dec 2024 16:17:30 +0000 https://datastreamer.io/?p=42316 Estimating the Cost to Adding Web Data Sources By Tyler Logtenberg Decemeber 2024 | 7 min. read Table of Contents To account for the estimated costs in the integration of a new data source into your product, there are three crucial elements to factor in. The first is the human resources required, the second being […]

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Estimating the Cost to Adding Web Data Sources

By Tyler Logtenberg

Decemeber 2024 | 7 min. read

Table of Contents

To account for the estimated costs in the integration of a new data source into your product, there are three crucial elements to factor in. The first is the human resources required, the second being the infrastructure costs, and lastly, the ongoing maintenance costs to sustain the new capability. 

Estimating Resource Costs

Different data sources can vary wildly, for the purpose of ensuring a simple and fair estimation, a general web data source is selected. An example of a source matching this criteria would be a news provider, blog network, or mid-sized social network. 

This estimation does not include the cost of data source acquisition, licensing, or the high-level enrichments required with web data. For an idea on the estimated costs to create an NLP/ML classified or model, we created another page for that which dives into the details similar to here. There is also a substantial amount of education around internal tooling and frameworks required as sometimes the provided SDKS are just not robust or well-documented. 

The average effort for the integration of a web data source can very in size, but often can be estimated at a duration of 3.5 “Sprints.” A Sprint is a measurement within engineering teams of dedicated time to specific stories and generally aligned with 2 week cycles. This brings our estimation of duration to 7 weeks from planning to production release. The usual composition and costs that are most common seen are laid out below. 

Resource Monthly Estimate Count
Software Engineer $12,442 2
DevOps Engineer $13,939 1
Resource Cost $38,823 3

Using this estimated 7-week duration of effort, the Resource Costs of the web data integration would be $67,940, and does not include other documentation, product marketing, QA, or project management costs. 

Infrastructure Estimated Costs

In addition to the resource costs, there are many supporting costs across infrastructure and supporting teams that may be applied.

The below estimation is illustrative of many of the regular costs, but does not include any costs around data enrichment beyond data structuring and schema unification.

InfrastructureMonthly EstimateOngoing
Transform Costs$150Yes
Extraction Costs$120Yes
Data Storage*$414Yes
DevOps Tools$1,000Yes
Infrastructure Cost$1,684

Using this estimated 7-week duration of effort, the supporting costs of the data source during the initial integration project would be $2,947.

*Data storage options vary, but the most common usage is a Search-focused database service such as BigQuery, ElasticSearch, or others. 100GB per month on a 3-month rolling cycle is used, priced at a per GB price of $1.38.

Estimated Maintenance Costs & Summary

Software Engineers working with external web data see a new release update every 6 weeks. As web data sources are subject to many changes and are in a state of rapid market changes, a side-effect of this rapid change leads to a breaking change per source every 18 months requiring extensive refactoring. In addition to the roughly 15% maintenance costs, budget should be set aside for refactoring every 18 months.

InfrastructureMonthly EstimateCommit %
Human Resources$48615%
Transform Costs$150Full
Extraction Costs$120Full
Data Storage$414Full
DevOps Tools$10010%
Maintenance Cost$1,269

The total costs summarized for a web data source integration are then best separated into the initial project costs and ongoing maintenance.

Estimated Web Data Integration Costs
Initial Data Source IntegrationOngoing Monthly Maintenance
$70,887 USD$1,269 USD

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]]> When Will My Company Outgrow Talkwalker? A Guide for Social Listening Products https://datastreamer.io/outgrowing-talkwalker-guide-for-social-listening-products/ Thu, 28 Nov 2024 17:42:14 +0000 https://datastreamer.io/?p=42131 For social listening products entering the market, Talkwalker’s APIs offer a foundational framework to bring the UI, familiar experience, and supporting APIs together.

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When Will My Company Outgrow Talkwalker? A Guide for Social Listening Products

By Tyler Logtenberg

Decemeber 2024 | 7 min. read

Table of Contents

Talkwalker Is An Ideal Initial Solution

For social listening products entering the market, Talkwalker’s APIs offer a foundational framework to bring the UI, familiar experience, and supporting APIs together. In the creation of other social listening products, these APIs become the source backbones of the platforms. Talkwalker’s APIs offer: multi-source data aggregation, basic enrichments, and an accessible taxonomy system.

Utilizing the APIs of another platform provides companies a way to integrate social and media data into their products, and leverage enrichment and search capabilities, without building a custom data pipeline from scratch. However, while Talkwalker meets the needs of many early-stage use cases, it’s often outgrown as companies mature and require greater flexibility, real-time data, and in-depth analysis capabilities.

Talkwalker’s API Capabilities: What It Can (And Can’t) Do For Scaling Companies

Before we can dive into an exploration of the “when”, we need to understand what it can and can’t do for scaling products. While Talkwalker provides basic social listening functionality, its constraints can become limiting as companies expand:

  1. Credit-Based Data Access: Talkwalker’s API operates on a credit-based system, meaning that data access is limited by credit availability. For high-frequency or high-volume data needs, companies may quickly hit credit limits, creating bottlenecks and additional costs as data needs grow.
  2. Rate Limits of 240 Calls per Minute: In the scaled industry, rate limits become a key technical limitation, and are often measured in calls per second. While Talkwalkers rate limits may be sufficient for basic monitoring, scaling platforms with higher volumes can quickly find these restrictive, especially during high-traffic events or crisis monitoring.
  3. Self-Managed Data Storage: Talkwalker doesn’t store API results, leaving companies responsible for their own data storage. This can become a significant burden for teams scaling beyond initial use cases, especially if they need both current and historical data at hand. Elements like trend prediction, influencer efforts, AI training, or even moderate analysis require large volumes of data.
  4. Export Limitations: Data export restrictions affect several key platforms, including Facebook, Instagram, LinkedIn, and Reddit. Additionally, metadata for Twitter and other sources is limited, often forcing companies to rely on separate APIs for richer insights. In some cases, the documentation of Talkwalker suggests going directly to different data sources outside of the Talkwalker platform!
  5. Limited Enrichments: Talkwalker does offer basic enrichments, including sentiment analysis, country filtering, basic image analysis, topics, and entity recognition. While these are helpful for early insights, they may fall short as companies seek more detailed or custom data tags, audience insights, or advanced sentiment scoring. They are also general enrichments common across the market, limiting scaling companies from creating product differentiation or customization.
  6. Time-Limited Search Results: The API’s search capabilities allow access only to the last 30 days of data, limiting long-term analysis and making it challenging to identify historical trends over time.
  7. Boolean Search Cap: With a cap of 50 boolean operands, Talkwalker’s search capabilities can be restrictive, especially for platforms seeking to conduct complex, multi-variable searches.

Key Indicators You’re Outgrowing Talkwalker

  1. Increasing Data Source Needs: Organizations may be able to work within Talkwalker’s source constraints, using around 6 categories of data, as startups. However, as companies move to the scale-up or growth stage, they often need access to a wider range of sources. Enterprise companies typically require access to about 16 source categories to meet comprehensive data coverage needs. For many, Talkwalker’s export limitations on major social media and review platforms restrict the breadth of insights they can provide, which becomes increasingly problematic with scale.

This table is specific to the Brand Monitoring industry focus, and showcases the size, requirements, and if they have outgrown Talkwalker. These metrics are an average and do not take into account pivots or niche specializations.

Company Size Bracket

Data Source Categories Required*

Likely Outgrown?*

Average Company Age*

0-50 (Startup)

6

No

1.8 years

51-150 (Scaler)

8

Yes

3.9 years

151-400 (Growth Leaders)

10

Yes

5.9 years

400+ (Market Titans)

16

Yes

8.8 years

*Specific to Brand Monitoring industry focus

  1. High Data Volume or Frequency Needs are Pushing Credit Limits: Companies with growing data needs often find themselves quickly depleting Talkwalker credits, particularly if they are pulling data from multiple sources or for multiple projects. For platforms needing continuous data access, credit limitations can create unplanned expenses or data gaps.
  2. Increasing Competitor Pressure: With many organizations relying on similar feature capabilities, the capabilities become commoditized between competitors. Increased competitor pressure, and churn, are often due to over-reliance on these commoditized capabilities.
  3. Loss of Engineering Product Focus: Talkwalker’s approach requires companies to handle their own data storage and management, which forces the technical teams of many organizations into considering and implementing “helper pipelines”. These efforts, which are not core to the offerings of the organizations, often cause spikes in engineering costs and delayed speed-to-market due to split focus.
  4. Need for Advanced Enrichment: As products mature, many require data enrichments beyond basic sentiment or topic identification. Companies that need granular sentiment analysis, detailed entity recognition, AI capabilities, or even custom enrichments may find Talkwalker’s offerings insufficient.
  5. Limited Historical Analysis: Talkwalker’s 30-day data window restricts long-term trend analysis, which is essential for companies needing to track patterns over months or years. If your platform is moving toward providing trend analytics, deeper insights, or historical comparisons, the API’s time limits could quickly become a constraint.

Migration: Paths for When Talkwalker no Longer Fits

For companies reaching the stage where Talkwalker’s API limitations are hindering product capabilities, the question becomes how to scale beyond it. Below are three common paths forward, from incremental shifts to full migrations.

  1. Hybrid Solution: Many companies take a gradual approach, retaining Talkwalker’s API for certain data sources while integrating a more flexible provider like Datastreamer for real-time or high-volume needs. Taking a “DIY” approach is a secondary option, but increases the need of “helper pipelines” which, if created and managed internally, can cause  “Pipeline Plateau” symptoms.
  2. Soft Upgrade: A phased approach allows companies to transition to a more advanced platform over time. By adding components from various parties into a Pipeline Orchestration Platform, companies can progressively migrate away from Talkwalker while minimizing disruptions and balancing resource requirements.

Full Upgrade: For mature platforms that have fully outgrown Talkwalker’s API limitations, a full upgrade to a new platform may be the best option. Moving entirely to a scalable, flexible Orchestration Platform like Datastreamer allows companies to bypass constraints such as rate limits and credit systems, while also gaining no-code abilities to add any enrichment, source, or capability required. This approach is ideal for companies needing a future-proof, high-powered data pipeline to support long-term growth.

Conclusion: Identify and Plan Migration before Product Stalling

Talkwalker provides a valuable entry point for companies launching social listening and media monitoring products, but its limitations often surface as companies scale and data needs evolve. From rate limits to export restrictions and limited enrichments, Talkwalker’s API can start to constrain the insights products that companies want to deliver.

In many cases, companies like Talkwalker use their own Pipeline Orchestration Platforms, and leveraging these underlying systems directly can be a massive benefit. 

Understanding limitations, identifying indicators, and beginning to plan and leverage migration is a critical step. It is important to avoid the “Pipeline Plateau” which may occur due to investing in-house capabilities in an effort to replicate Talkwalker capabilities. Leveraging a Data Orchestration Platform like Datastreamer is the correct decision to make.

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]]> Instagram APIs for Custom Monitoring | Official API v.s. Alternative API v.s. Scraping https://datastreamer.io/instagram-data-guide-official-vs-alternative-api-vs-scraping/ Wed, 09 Oct 2024 18:07:09 +0000 https://datastreamer.io/?p=40977 Instagram APIs for Custom Monitoring (Official vs Alternative APIs vs Scraping) By Juan Combariza October 2024 | 12 min. read Table of Contents Skip to Section Official Instagram API Alternative Instagram APIs (Third-Party) Instagram Scraping Instagram API for Social Listening Behind the brunch selfies and fashion haul posts, Instagram is ripe with rich information around […]

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Instagram APIs for Custom Monitoring (Official vs Alternative APIs vs Scraping)

juan-combariza-picture

By Juan Combariza

October 2024 | 12 min. read

Table of Contents

Skip to Section

  1. Official Instagram API
  2. Alternative Instagram APIs (Third-Party)
  3. Instagram Scraping
Instagram APIs Graphics - Insights Architecture - Instagram API Alternatives

Instagram API for Social Listening

Behind the brunch selfies and fashion haul posts, Instagram is ripe with rich information around audience sentiment. Instagram is the fourth most visited website in the world, with an estimated 62.7% of users following or researching brands on the platform. Large organizations have kept a pulse on social conversations for years, but we’ve seen a surge in demand for insights teams (services or software platforms) to develop customized intelligence capabilities. This is made possible by hooking directly into an Instagram API, which facilitates access to raw Instagram data and allows for the manipulation of this data to craft customized intelligence outputs.

Instagram API’s are often used to feed custom reports, dashboards, or proprietary AI models:

  • Trend Prediction for Fashion: An insights platform might use predictive AI models that forecast upcoming fashion trends based on Instagram data. This enables their fashion brand customers to stay ahead of the curve by adapting their designs and inventory accordingly. 
  • Market Strategy Reports for Brands: A large marketing agency collaborates with a Fortune 500 brand to collect extensive online data, offering deeper insights than traditional focus groups. This comprehensive view reveals customer perception towards products & marketing campaigns.
  • Threat Intelligence Monitoring: A threat intelligence platform monitors online conversations on Instagram to detect potential threats to a company, individual, or corporation which could range from cyber threats to physical threats. 
juan-combariza-picture

Note from the author:

Our platform facilitates the integration of these APIs, so we’ve helped dozens of data product teams, outsourced dev. agencies, and in-house insights departments build pipelines to connect Instagram data into bespoke tools. I wrote this blog to outline the different data access methods available, assessing data capabilities and setup effort, with a focus on custom social media monitoring as the primary application.

Understanding Instagram Data Access

What is an Instagram API?

An Instagram API (Application Programming Interface) is a set of tools that allow developers to interact with the functionalities and data of Instagram. Think of it as a bridge between Instagram’s extensive database and your own applications. APIs can also be used as a way to enable functionalities in a product, such as automated scheduling. 

The focus of this blog is on the extraction of insights (instead of other API functionalities like post scheduling or account management).

Instagram API v.s. Social Listening Tools

Tools like Meltwater and Brandwatch allow brands to easily monitor Instagram conversations through pre-configured, code-free setups. In contrast, Instagram APIs present a low-code solution to integrate data feeds into custom tools. This approach offers granular customizability of data flows and the ability to implement deeper AI enrichments, differentiating your social listening solutions from existing players.

Instagram API Integration Methods & Costs

Source: Cost Savings Analysis of Pre-Built Pipeline Infrastructure

Data collection is only the first step in the supply chain of insights. You will still need a data pipeline infrastructure that will move and refine the raw information into clear intelligence.

Consider this simplified pipeline model:

sample-instagram-pipeline-skeleton

Option A: Build your own API infrastructure

While constructing REST API connectors from vendors into your systems seems straightforward, this approach often only addresses the initial and final stages of data handling (steps 1 and 6), potentially leaving gaps that impact the quality of insights you provide to your customers.

Option B: Pre-built pipeline platform

Pre-built pipeline components significantly cut down the time needed to add sophisticated data control into your social insights pipelines. Instead of individually maintaining 6-7 different API connectors (blogs feeds, news feeds, social feeds), you can consolidate them into 1 platform that enriches data utility and increases its strategic value.

Option 1: Official Instagram API

Instagram-API-data-fields
Overview: 

The official Instagram API, developed and maintained by Instagram itself, is crafted to offer regulated and structured access to the platform’s extensive data. This API is designed to ensure that third-party developers and businesses can interact with Instagram’s features and data in a way that upholds the platform’s strict data privacy rules and user protections.

Capabilities
  • User Profile Access: Basic information on profiles that you manage, including user IDs, usernames, bios, and profile pictures.
  • Post Metadata: Caption, media type, media URL, timestamp, hashtags and tagged users.
  • Engagement Metrics: Likes, comments, tagged users in comments, and shares for posts made by an account you manage.
  • Account Analytics: For business and creator accounts: Post performance (reach and performance), audience demographics, and other metrics you would see in the “insights” tab of your IG account.
  • Brand tags: You can retrieve posts where a brand or Business/Creator account is tagged (with an @), but this is restricted to content directly related to the accounts you manage.
  • Hashtag searches: The only form of public data search that is available through the Instagram Graph API is hashtag search.
  •  
Limitations
  • Restricted to accounts you manage: Does not support the search of content based on location, keywords, or entity mentions.
  • Only Identifies Direct Mentions: You will only be able to identify captions, comments, and media where an account has been directly tagged or @mentioned.
  • Lack of user profile data: With the Instagram Graph API, you do not have access to any user profile metadata for profiles that you do not manage or own
  • Rate Limits: The Instagram API enforces rate limits that cap the number of requests an application can make within a given time frame.
  • Historical search: The Instagram Graph API provides limited access to historical data, as it is primarily focused on insights and analytics for Business and Creator accounts

Instagram Graph API v.s. Basic Display API

Instagram Basic Display API: Restricted to personal accounts, this API doesn’t apply to Creator or Business profiles. It allows you to pull data solely from personal accounts that you’ve authenticated with login access. A typical scenario is using this API to display a personal Instagram feed on a website.

Instagram Graph API: The Graph API is designed for Instagram Business and Creator accounts. It is meant for businesses to retrieve data on posts, comments, and follower demographics for posts made by a business account you are managing.

Note: On September 4, 2024, Meta announced that the Instagram Basic Display API would become deprecated. You can retrieve data through the Instagram API with Instagram Login

Official Instagram API Pricing

There is no direct cost associated with accessing the Instagram Graph API itself, as it is provided by Facebook (Meta) for free. The actual use of the API can carry indirect costs, including the wages for developers and the overheads for server and pipeline infrastructure. Although it is free, using the Instagram Graph API does require approval from Instagram and comes with API rate limits that vary based on your access level.

Option 2: Instagram API Alternatives (Third-Party APIs)

Datastreamer - APIs for Instagram Data - Sample API Request
What is a third-party data collector?

Third-party APIs, or “unofficial APIs”, are often favored for social listening as they gather extensive public data (posts, comments, user profiles) with their own independent collection methods. This data can then be queried or integrated through API commands, allowing access to a wide array of metadata that the official Instagram API lacks.

Third Party APIs typically do not require you to set up any scraping, greatly reducing legal and compliance risks.

Building Custom Instagram Monitoring for Your Clients?

The quality of third-party APIs varies widely. There is no shortage of horror stories of poorly maintained API environments, low data quality, or an inability to stay up to date with changes in Instagram’s platform. Datastreamer is not a data provider, but we’ve worked with dozens of third-party APIs and streamline your integration process:

  • Tap into our pre vetted network of data providers
  • See custom data engineering components that differentiate
your insights from existing tools like Brandwatch
  • Test drive a pipeline to run pricing scenarios with real-world usage metrics
Datastreamer - Catalog Grid - Icon
We Build You a Free Pipeline
Third-Party API Data Fields

Available metadata changes based on which third-party API you are using. This list is based on the Instagram data partners we’ve worked with in the past:

  • Search Instagram Profiles: Profile name, profile URL, biography, links in bio, verified status, engagement metrics (followers, post count).
  • Search Instagram Posts: Media type, media URL, captions, hashtags, mentions, comments, engagement metrics (likes and shares), timestamps.
  • Monitor Real-Time Instagram Data: Access real-time data to gain proactive insights, such as establish alerts for customer service or assess real-time perceptions concerning entities.
  • Search Historical Instagram data: Depending on the vendor, access historical data that can span back several years. Feed this into trend or sentiment analysis that looks at conversation topics over time.
Capabilities (Social Listening)
  • Monitor Instagram Keywords & Phrases: Track specific keywords and phrases across social media posts and comments. For example, track “sustainable fashion” to analyze how often it’s discussed across social platforms and understand the sentiment around sustainable materials in the apparel industry.
  • Monitor Instagram Profiles: Keep tabs on the activity of specific user profiles, including updates, posts, and public interactions. For example, monitor the profile of influencer (@janedoe) to observe engagement trends and the effectiveness of her promotional posts for various brands.
  • Monitor Instagram Hashtags: Follow specific hashtags to capture all related content, providing insights into discussions around a specific topic. For example, follow the hashtag #TechInnovation2024 to gauge pre-event buzz and attendees expectations.
  • Monitor Instagram Brand Mentions: Automatically detect and analyze mentions of a brand across social media to understand audience perception. For example, set up alerts for any mentions of “Starbucks” to gather feedback on new product launches or store openings.
  • Monitor Instagram Mentions of Products, Places, People: Use Entity Recognition, an AI model with greater accuracy than keyword searches, to track mentions of products, places, or notable individuals to gather detailed insights into the perception and popularity of these subjects. For example, track mentions of “Tesla Model Y” across social media to collect user opinions and common issues.

Capabilities (Data Enhancement)

  • Customization in collection: Certain vendors permit collection customization requests, such as increasing the frequency of collection for a list of specific profiles.
  • Enrichments: Some third-party APIs include standard data enrichments like sentiment analysis and entity recognition. Advanced enrichments, like detecting action intent, enhancing location data, or translating languages, can be added by using a pipeline platform.
  • Multiple Platforms Supported: Many third-party APIs collect data from multiple social media platforms, providing broader coverage for a more complete analysis of social conversations.
  • Advanced filtering: With a pipeline platform, raw Instagram data feeds can be filtered and routed based on metadata conditions. For example, data streams can be distilled down to core elements (keywords/phrases), and then have all results translated into English.
Limitations
  • Data Interruptions: Since alternative APIs collect data as a third-party, a major Instagram platform update may disrupt collection while data aggregators adjust their tech to align with new changes.
  • Data Quality: Data integrity is dependent on the technology employed to collect it. Less sophisticated third-party APIs may only capture only a limited subset of the available data.
  • Developer Friendliness: Poorly built API environments can slow down speed-to-market speeds and create a recurring headache for developers tasked with maintaining integrations.
Are Third-Party Instagram APIs Legal?

Leveraging third-party APIs to access public data is generally legal and commonly utilized by large companies. Nonetheless, conducting proper due diligence remains important:

  • Understand compliance requirements with local privacy laws (GDPR or CCPA), as these regulations govern how personal data can be collected, stored, and processed. 
  • Ensure the intended use of the data and the handling of information within your pipeline align with legal and ethical guidelines.
Pricing for Instagram API Alternatives 
  • Usage based data consumption: Pricing often depends on the volume of data accessed via API calls. Prices are tied to the scope of data queries, such as hashtag searches, profile analysis, and the choice between historical or real-time data access.
  • Pipeline infrastructure costs: There are costs associated with the underlying infrastructure required to support the data pipeline. This includes servers, data storage, and the network resources needed to process and handle the data streams efficiently.
  • Integration setup & maintenance:  Labor costs are involved in both the development of API connectors and their ongoing maintenance to ensure stable connections to diverse data sources
Running a Pilot Pipeline To Forecast Costs

Estimating the exact costs of using third-party Instagram APIs can be complex due to the variability in data usage and API call frequency. Since insights teams rarely know their exact data usage ahead of time, the most effective method to predict expenses is by conducting a pilot test.

Running a scaled-down version of the intended data pipeline allows teams to gather actual usage statistics, providing a realistic basis for cost projections

Option 3: Instagram Scraping

What is Instagram Scraping?

Instagram scraping is a method where data is programmatically extracted directly from the web pages of Instagram. This technique involves writing scripts or using software that simulates the actions of a web browser to gather visible data from the platform’s frontend. While scraping can provide access to a wide range of data that might not be available through official channels, it requires a solid understanding of both programming and the legal implications involved.

Capabilities
  • Comprehensive Data Extraction: Scrapers can be tailored to collect detailed information from Instagram, such as user comments, post timings, and hashtag usage, which are visible on public profiles and pages.
  • High Customizability: Since scraping scripts are custom-built, they can be designed to meet specific data requirements, targeting exactly what is needed without redundancy.
Limitations
  • Fragility of Setup: Instagram frequently updates its site layout and underlying code, which can render scrapers obsolete overnight. This requires constant maintenance of scraping scripts to ensure they remain effective.
  • Legal and Compliance Risks: Scraping data from Instagram can breach the terms of service set out by the platform, potentially leading to legal actions or bans from the site. Moreover, data privacy regulations like GDPR and CCPA impose additional layers of compliance, which scraping might violate.
  • Data Integrity Issues: Data collected via scraping is only as good as the scraper’s design and the public visibility of data. Automated scrapers may not always interpret page layouts and data formats consistently, particularly if Instagram changes its interface.
Costs
  • Initial Setup Costs: While starting costs for scraping can be minimal—especially if using open-source tools or low-code tools—the real investment is in the development of robust scraping scripts.
  • Maintenance Expenses: Ongoing costs can escalate due to the need for regular updates and troubleshooting of scraping scripts to keep up with changes on Instagram’s platform.
  • Infrastructure Costs: Establishing custom scrapers addresses the initial data collection needs, but a real-time data pipeline to power an insights solution involves additional infrastructure. This adds overhead costs for data handling, processing, and storage.
Strategic Considerations

While scraping might seem like a low-cost solution for accessing extensive data from Instagram, it comes with significant operational and legal risks that can affect its overall viability and sustainability. Businesses considering this approach must carefully evaluate their capability to manage these risks and the potential impact on their operations and reputation.

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