Datastreamer Data Pipelines for Unstructured Data Mon, 17 Mar 2025 08:38:04 +0000 en-US hourly 1 https://datastreamer.io/wp-content/uploads/2022/04/cropped-DATASTREAMER-2048x331-1-32x32.png Datastreamer 32 32 Unlocking Data Insights: The Power of Volume Extrapolation in Your Datastreamer Pipeline https://datastreamer.io/unlocking-data-insights-volume-extrapolation-datastreamer/ Mon, 17 Mar 2025 08:35:03 +0000 https://datastreamer.io/?p=43974 Unlocking Data Insights: The Power of Volume Extrapolation in Your Datastreamer Pipeline By Nadia Conroy March 2025 | 15 min. read Table of Contents In the world of data-driven decision-making, knowing the scale of content in a third-party data source is crucial. Whether you’re analyzing social media trends, monitoring online discussions, or estimating market activity, […]

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Unlocking Data Insights: The Power of Volume Extrapolation in Your Datastreamer Pipeline

Nadia-Conroy

By Nadia Conroy

March 2025 | 15 min. read

Table of Contents

In the world of data-driven decision-making, knowing the scale of content in a third-party data source is crucial. Whether you’re analyzing social media trends, monitoring online discussions, or estimating market activity, the ability to extrapolate data volume accurately can shape strategic decisions. 

Volume extrapolation provides a structured methodology to estimate document volumes using a number of different methods, depending on the accuracy required. By running these scenarios in a pipeline, businesses can gain insights into content patterns, optimize data collection, and scale operations efficiently. 

Let’s explore the details of implementing volume extrapolation and why this is useful to potential customers. 

Why Volume Extrapolation Matters

For businesses relying on external data sources, volume extrapolation answers key questions: 

  • How much data is available? 
  • What are the content trends over time? 
  • How can we optimize data collection and reduce API costs? 
  • Can we predict data availability for future projects? 

By applying volume extrapolation techniques from a data pipeline, businesses can make informed decisions, ensuring they gather the right amount of data without overspending or missing critical insights. 

Business Scenario: Market Research & Social Listening

Consider a marketing analytics firm specializing in social listening. The company provides insights to brands about their market presence, customer sentiment, and trending topics on platforms like Instagram, Twitter, and TikTok. Their clients depend on accurate data volume estimates to determine:

  • How many mentions a brand receives daily?
  • When and where conversations peak?
  • What volume of data do they need to collect to track a campaign effectively
The Challenge

The firm needs to analyze online discussions about a new product launch such as a phone device. If they overestimate data volume, they may waste resources collecting excessive, unnecessary data, increasing storage and processing costs. If they underestimate, they risk missing crucial trends, leading to incomplete insights that misguide their clients.

How Volume Extrapolation Solves This Problem

By applying volume extrapolation, they can:

  1. Estimate Daily and Weekly Post Volumes: By collecting controlled time samples, they predict expected post volumes without exhaustive data collection.
  2. Identify Peak and Off-Peak Hours: Knowing when audiences are most active helps optimize monitoring strategies.
  3. Forecast Future Data Needs: A campaign’s social media impact can be estimated over time, helping allocate resources efficiently.
  4. Control Costs: Instead of making excessive API calls, they can optimize queries based on expected content volume

Performing Volume Extrapolation for Your Pipeline

The accuracy of your volume estimation depends on the chosen approach. Let’s explore the three levels of accuracy and how they can be integrated into a data pipeline.

Reduced Accuracy: Quick Estimates for Initial Scoping

This approach provides a high-level estimate, ideal for feasibility checks or project scoping. The linear scaling method used here is the least precise but is fast and cost-effective.

Use Case:  Businesses wishing to explore a data source and can use this method to quickly assess whether the data volume justifies further investment.

Implementation:

  • Create a pipeline 
    For data ingestion we can setup a pipeline with a data Ingress from a selection of sources, such as  Bright Data Instagram, Bluesky Social Media, or Socialgist TikTok
    • Configure the pipeline Ingress with keyword query describing the product launch, such as (“XPhone Pro” OR “#XPhonePro”)
    • Make it a One Time job with a  target limit of documents, for example 1,000 posts.
    • Add the Unify Transformer component to standardize the data and time format.
    • Add an Egress component utilizing the Datastreamer Searchable Storage component for easy API access to analyze the data. For a smaller sample size, the Document Inspector would also be a viable option.
  • Analyze time distribution
    Suppose the collection window spans 4 hours from the first to the last post:
    • First post timestamp: 2025-03-08 10:15 AM UTC 
    • Last post timestamp: 2025-03-08 2:15 PM UTC
    • Total posts collected: 1,000 over a time span of 4 hours, or about 250 posts per hour
  • Extrapolate the volume:  
    We can now scale this to a monthly count.  
    • 250 per hour is approximately 6,000 (250 x 24) posts per day, or 180,000 (6,000 x 30) posts per month

Medium Accuracy: Balanced Sampling for Better Insights

For better accuracy on tasks, such as how to identify online purchasing or interest trends, this method provides a balance between accuracy and efficiency without requiring continuous data collection. Instead of collecting 24/7 data, we instead use 1-hour snapshots at 6-hour intervals over 3 days, then extrapolate the overall volume.

Use Case: A content monitoring company analyzing regional engagement trends can use this method to detect peak usage hours across different markets.

Implementation:

Step 1: Setup a Pipeline with the same ingress, keyword query and unify component as you would in the reduced accuracy method.

Step 2: Schedule jobs to collect data with set fixed sampling windows: 
Configure the job to collect 1-hour samples every 6 hours over 1-3 days. You may wind up with collected data that looks like this, showing average per hour posts across all days.

Time BlockDay 1Day 2Day 3Average per hour
12am – 1am730760750740
6am – 7am830840880850
12pm – 1pm1250147015101410
6pm – 7pm1730168016601690

Step 3: Estimate Total Daily Volume: 
(740 + 850 + 1410 + 1690) / 4 = 1170 average posts per hour x 24 hours =  

  • that’s approximately 28 K posts per day
  • or roughly 840K posts per month

Step 4: Enhance with enrichments: 

Once the volume estimation is set, we can integrate classifiers to apply metadata on the selected content, for example:

  • Language distribution (e.g., English vs. Spanish content)
  • Topic segmentation (e.g., product reviews vs. general discussion)
  • Geographic analysis (e.g., North America vs. Europe)

This adds contextual insights beyond just raw volume estimation

High Accuracy: Continuous Data Collection for Precision

This approach provides the highest level of accuracy by running a continuous 24/7 data ingestion pipeline over a 7-day period. It captures all variability in content volume, including hourly, daily, and event-driven fluctuations.

Step 1: Create a Continuous 24/7 Pipeline

Since the pipeline described above, configure a job to collect all data on your keyword query over a 7 day period. 

Step 2: Analyze Daily Volumes (Peak vs. Off-Peak Days)

Once a full 7 days of data is collected, we analyze total post volume per day to distinguish between peak and off-peak patterns.

Example Breakdown of Daily Post Volumes
DayTotal Posts Collected
Monday14,000
Tuesday16,400
Wednesday13,300
Thursday17,200
Friday19,600
Saturday24,300
Sunday27,000

From this, we classify peak and off-peak days:

  • Peak Days: Friday, Saturday, Sunday
  • Off-Peak Days: Monday – Thursday

This tells us that weekends have significantly higher activity, likely due to more free time for users to engage with content.

Step 3: Segment Hourly Patterns (Peak vs. Off-Peak Trends)

To refine our extrapolation, we compute average post volume per hour separately for peak and off-peak days.

Example: Average Posts Per Hour on Peak Days
HourAverage Posts (Peak Days)
12am – 1am760
6am – 7am890
12pm – 1pm1340
6pm – 7pm1600
Example: Average Posts Per Hour on Off-Peak Days
HourAverage Posts (Off-Peak Days)
12am – 1am630
6am – 7am560
12pm – 1pm920
6pm – 7pm1240

This shows that activity is much higher in the evenings and midday on peak days, while off-peak days have lower activity across all time slots.

Step 4: Handle Anomalies (Filtering Out Viral Event Spikes)

A major event, such as a celebrity endorsement, controversy, or viral trend, can cause short-term spikes in post count that may distort the extrapolation.

For example, a viral event such as a tech influencer who posts an unboxing video of the new phone, causing a massive spike in social media posts. Instead of the usual 15,000 posts on a weekday, we suddenly get 50,000 posts in a single day.

By filtering out posts by explicitly excluding keywords in a search query, such as “unboxing”, “lawsuit”, or “MKBHD hands-on” we have a chance of separating content that indicates an uncharacteristic spike in content. When there is a significant percentage of posts that contain these keywords, we could exclude that day from the baseline calculations and achieve a more typical daily volume. 

Step 5: Apply Weighted Averages to Scale Monthly Estimates

Now that we have clean daily volume estimates, we scale up to monthly projections using a weighted formula.

  • Weekday average: 15,000 posts/day
  • Weekend average: 25,000 posts/day
  • Number of weekdays in a month: 22
  • Number of weekends in a month: 8

Final Monthly Estimation Calculation

 = (15,000 posts x 22 days) + (25,000 x 8 days) = 530,000 posts per month.

Benefits To New Datastreamer Customers

By leveraging these approaches, businesses using Datastreamer pipelines can plan data collection efficiently, avoid unnecessary costs, and gain deep insights into social media activity.

Whether performing a quick feasibility check or a long-term analysis, volume extrapolation provides a powerful framework for data-driven success.

  • Optimized Data Collection: Avoid excessive API calls while ensuring sufficient data coverage.
  • Improved Forecasting: Predict trends and ensure data availability for future needs.
  • Cost Savings: Reduce unnecessary processing and storage costs.
  • Scalability: Establish a repeatable, automated methodology that grows with business needs.

Accurate volume extrapolation allows businesses to more accurately forecast social media trends, optimize data pipelines, and make cost-effective decisions. Whether you’re conducting market research, tracking brand sentiment, or monitoring industry trends, applying a variety of approaches from your Datastreamer dynamic pipeline ensures that, with informed extrapolation, the right balance is struck between data collection efficiency and actionable insights.

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]]> 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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