9 Helpful Tools for Building a Data Pipeline

Companies create tons of disparate data throughout their organizations through applications, databases, files and streaming sources. Moving the data from one data source to another is a complex and tedious process. Ingesting different types of data into a common platform requires extensive skill and knowledge of both the inherent data type of use and sources.

Due to these complexities, this process can be faulty, leading to inefficiencies like bottlenecks, or the loss or duplication of data. As a result, data analytics becomes less accurate and less useful and in many instances, provide inconclusive or just plain inaccurate results.

For example, a company might be looking to pull raw data from a database or CRM system and move it to a data lake or data warehouse for predictive analytics. To ensure this process is done efficiently, a comprehensive data strategy needs to be deployed necessitating the creation of a data pipeline.

What is a Data Pipeline?

A data pipeline is a set of actions organized into processing steps that integrates raw data from multiple sources to one destination for storage, business intelligence (BI), data analysis, and visualization.

There are three key elements to a data pipeline: source, processing, and destination. The source is the starting point for a data pipeline. Data sources may include relational databases and data from SaaS applications. There are two different methods for processing or ingesting models: batch processing and stream processing.

  • Batch processing: Occurs when the source data is collected periodically and sent to the destination system. Batch processing enables the complex analysis of large datasets. As patch processing occurs periodically, the insights gained from this type of processing are from information and activities that occurred in the past.
  • Stream processing: Occurs in real-time, sourcing, manipulating, and loading the data as soon as it’s created. Stream processing may be more appropriate when timeliness is important because it takes less time than batch processing. Additionally, stream processing comes with lower cost and lower maintenance.

The destination is where the data is stored, such as an on-premises or cloud-based location like a data warehouse, a data lake, a data mart, or a certain application. The destination may also be referred to as a “sink”.

Data Pipeline vs. ETL Pipeline

One popular subset of a data pipeline is an ETL pipeline, which stands for extract, transform, and load. While popular, the term is not interchangeable with the umbrella term of “data pipeline”. An ETL pipeline is a series of processes that extract data from a source, transform it, and load it into a destination. The source might be business systems or marketing tools with a data warehouse as a destination.

There are a few key differentiators between an ETL pipeline and a data pipeline. First, ETL pipelines always involve data transformation and are processed in batches, while data pipelines ingest in real-time and do not always involve data transformation. Additionally, an ETL Pipeline ends with loading the data into its destination, while a data pipeline doesn’t always end with the loading. Instead, the loading can instead activate new processes by triggering webhooks in other systems.

Uses for Data Pipelines:

  • To move, process, and store data
  • To perform predictive analytics
  • To enable real-time reporting and metric updates

Uses for ETL Pipelines:

  • To centralize your company’s data
  • To move and transform data internally between different data stores
  • To Enrich your CRM system with additional data

9 Popular Data Pipeline Tools

Although a data pipeline helps organize the flow of your data to a destination, managing the operations of your data pipeline can be overwhelming. For efficient operations, there are a variety of useful tools that serve different pipeline needs. Some of the best and most popular tools include:

  • AWS Data Pipeline: Easily automates the movement and transformation of data. The platform helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available.
  • Azure Data Factory: A data integration service that allows you to visually integrate your data sources with more than 90 built-in, maintenance-free connectors.
  • Etleap: A Redshift data pipeline tool that’s analyst-friendly and maintenance-free. Etleap makes it easy for business to move data from disparate sources to a Redshift data warehouse.
  • Fivetran: A platform that emphasizes the ability to unlock faster time to insight, rather than having to focus on ETL using robust solutions with standardized schemas and automated pipelines.
  • Google Cloud Dataflow: A unified stream and batch data processing platform that simplifies operations and management and reduces the total cost of ownership.
  • Keboola: Keboola is a platform is a SaaS platform that starts for free and covers the entire pipeline operation cycle.
  • Segment: A customer data platform used by businesses to collect, clean, and control customer data to help them understand the customer journey and personalize customer interactions.
  • Stitch: Stitch is a cloud-first platform rapidly moves data to the analysts of your business within minutes so that it can be used according to your requirements. Instead of focusing on your pipeline, Stitch helps reveal valuable insights.
  • Xplenty: A cloud-based platform for ETL that is beginner-friendly, simplifying the ETL process to prepare data for analytics.

 

How We Can Help

Building a data pipeline can be daunting due to the complexities involved in safely and efficiently transferring data. At 2nd Watch, we can build and manage your data for you so you can focus on BI and analytics to focus on your business. Contact us if you would like to learn more.

Why You Need to Modernize Your Media Supply Chain

The demand for direct-to-consumer services and media content is continuously growing, and with that, audiences are raising their expectations of media and entertainment companies. Agile and innovative companies, such as Netflix, YouTube, and Amazon Prime, have arguably created and continue to enable the current viewership trends.

These streaming services have disrupted the traditional media landscape by empowering audiences to watch any content wherever and whenever they want. To accommodate new audience behaviors, relevant media companies use technologies to support the modern-day digital media supply chain, which has become increasingly complex to manage.

However, legacy media companies have something that audiences still want: content. Most of these institutions have massive budgets for content production and enormous existing media libraries that have latent revenue potential. For example, legacy media brands own nostalgic cult classics, like “The Office,” that viewers will always want to watch, even though they have watched these episodes multiple times before.

As the volume of content consumption and demand increases, media organizations will find that a traditional media supply chain will constrain their ability to grow and meet customers in their preferred venues, despite owning a broad range of content that viewers want to watch. In order to keep up with audience demand, media companies will need to transform their media supply chains, so that they can distribute their media quickly and at scale, or they risk falling behind. Cloud technologies are the key to modernizing digital asset management, metadata models, quality control, and content delivery networks.

The Challenges of a Traditional Media Supply Chain

There are a lot of moving parts and behind-the-scenes work for media and entertainment businesses to push media assets to audiences. The media supply chain is the process used to create, manage, and deliver digital media from the point of origin (creator, content provider, content owner, etc.) to the destination (the audience.) For the right content and best experience to reach users on devices and platforms of their choice, digital media files must pass through various stages of processing and different workflows.

Media supply chain management is challenging and if there are inefficiencies within this  process, issues that will ultimately affect the bottom line will crop up. The following are top challenges of media supply chain management:

Decentralized Assets

The content wars are in full swing, and as a result, the media and entertainment industry has seen an influx of divestitures, mergers, and acquisitions. Organizations are accumulating as much content as possible by bolstering their media production with media acquisition, but as a result, content management has become more difficult. With more content comes more problems because this introduces more siloed third-party partners. As companies merge, the asset management system becomes decentralized, and media files and metadata are spread across different storage arrays in different datacenters that are managed by different MAMs with various metadata repositories.

Reliance on Manual Processes

Legacy media companies have been around much longer than modern technologies. As a result, some of these organizations still do many media production and distribution tasks manually, especially when it comes to generating, reviewing, and approving metadata. Metadata is essential for sorting, categorizing, routing, and archiving media content, as well as making the content accessible to a global, diverse audience. Using manual processes for these functions not only severely slows down a business, but they are also susceptible to human-error.

Quality of Media Assets

Today, consumers have the latest technology (4K TVs, surround sound systems, etc.), which requires the highest quality version of content sources. With dispersed content libraries and team, working derivative edits to meet localization and licensing requirements and locating native frame rate masters can be a challenging and time-consuming problem to tackle.

Benefits of Using Cloud Technology to Modernize the Media Supply Chain

Cloud-based technologies can help manage and resolve the issues typically encountered in a media supply chain. If media organizations do not utilize cloud solutions to modernize their supply chain, they risk being less agile to meet global audience demand, incurring higher costs to deliver media, and eroding viewership.

Legacy media brands are recognizing the consequences of not adopting modern technology to support their media supply chains, and recently, we’ve seen established media corporations partnering with cloud service providers to undertake a digital transformation. A recent and newsworthy example of this is the MGM and AWS partnership. MGM owns a deep library of film and television content, and by leveraging AWS, MGM is able to distribute this content with flexibility, scalability, reliability, and security to their audiences. AWS offers services and tools to modernize MGM’s media supply chain to be able to distribute content across multiple platforms quickly and at scale.

Businesses don’t need to strike historic deals with cloud service providers to receive the same benefits. By transforming into a cloud-based framework, any media company can reap the following major benefits of modernizing their media supply chain:

Scale and Agility

This point cannot be repeated enough because, again, customer media consumption is rapidly increasing, and businesses must find a way to meet those demands in order to retain customers and remain competitive. With cloud computing, the media supply chain is no longer limited to the capacity of on-premise data centers or the capital expenditure budget that was forecasted a year earlier. Using cloud technology allows organizations to be dynamic and flexible to adjust for growing demand. Businesses can easily scale services up (or even down) based on audience demands by simply adding (or removing) more cloud resources, which is easier and more forgiving than having to add more infrastructure or being stuck with wasted databases.

Cost Effective

Cloud services employ pay-as-you-go billing, which allows companies to pay for what they use rather than paying a fixed cost that may not fit their needs later on down the road. Most importantly, using the cloud removes the maintenance and operational costs associated with  maintaining data center footprints. The costs of server hardware, power consumption, and space for traditional data centers can really add up, especially because these costs are inflexible based on actual consumption. Utilizing cloud technology provides flexibility in billing and trims down maintenance costs.

Automation and Efficiency

Cloud services offer tools that can handle abstract operational complexities, like metadata management, that were historically done manually. These automation and AI features can dramatically reduce the need to manually generate this metadata because it implements machine learning and video, audio, and image recognition to largely automate the generation, review, and approval of metadata. Harnessing the power of automation frees up teams’ resources and time and redirects that energy on impactful, business-differentiating activities.

Data-Driven Decisions

Large audiences also means large amounts of data. Massive volumes of both structured and unstructured data requires increased processing power, storage, and more. Cloud computing has the scalable infrastructure to rapidly manage huge spikes of real time traffic or usage. Moreover, cloud service providers offer a variety of analytic tools that enable extract, transform, and loading of enormous datasets to provide meaningful insights quickly. Media companies can harness this data to improve user experiences and optimize supply chains, all of which greatly affects their bottom line.

How do I Get Started in my Media Supply Chain Transformation?

The process is less daunting than you think, and there are experienced cloud advisors and consulting firms who can point you in the right direction. At 2nd Watch, we embrace your unique modernization journey to help transform and modernize your business and achieve true business growth through cloud adoption. To learn more about our media cloud services, visit our Media and Entertainment page or talk to someone directly through our Contact Us page.

Why Cloud Services are Here to Stay for Media & Entertainment

During the COVID-19 pandemic, media and entertainment (M&E) organizations accelerated their need to undertake a digital transformation. As we approach a post-pandemic world, M&E companies are realizing that their digital transformation is no longer just a short-term solution, but rather, it is a long-term necessity to survive the increasingly competitive and saturated landscape of content distribution and consumption. Cloud service providers play a crucial role to M&E brands as they continue their digital evolution. Throughout the pandemic, cloud solutions allowed M&E companies to adapt efficiently and effectively. Beyond the landscape of COVID-19, a cloud-based framework will continue to facilitate agility and scalability in the M&E business model.  

How COVID-19 Impacted the Media and Entertainment Industry

When COVID-19 created an unprecedented environment and altered our daily operations, people and businesses had to rapidly adjust to the new circumstances. In particular, the M&E industry faced a reckoning that was imminent before the pandemic and became more acute during the pandemic.

For M&E businesses, COVID-19 forced upon them an important pivotal point in their digital strategy. The pandemic didn’t present vastly new challenges for M&E organizations, it simply accelerated and highlighted the problems they had already begun experiencing in the last five or so years. Viewer behavior is one of the biggest shake-ups in the M&E industry. Prior to 2020, audiences were already hunting for new ways to consume content. Traditional linear broadcast was waning and modern digital streaming services were booming. Media content consumption was drastically changing, as audiences streamed content on different devices, such as their smartphones, tablets, connected TVs, PCs, and gaming consoles. Now, legacy M&E brands are no longer competing just against nimble new players in the streaming space, but they are also competing against music, gaming, and esport platforms. All of these trends that were in motion pre-pandemic became more apparent after society began sheltering-in-place.

With most of the United States going remote, industry giants, like Warner Brothers and Disney, pivoted their focus to streaming content to adjust to shelter-in-place orders. In an unprecedented move, Warner Brothers began releasing new movies in theaters and via streaming platforms simultaneously. Disney’s emphasis on its streaming service, Disney Plus, paid off:  it exploded during quarantine and quickly accumulated 100 million subscribers. Additionally, Disney also followed a similar cinema distribution model to Warner Brothers by releasing new hits via streaming rather than just in theaters. 

The need for digital innovation was crucial for the M&E industry to adapt to the new circumstances created by the pandemic, and this need will continue long into the post-COVID world. M&E organizations faced a catalyst in their structural transformation, and the digitization of content workflows and distribution became absolutely imperative as employees went remote and content consumption hit an all-time high. Moreover, certain market trends were felt more acutely during the pandemic and represented a paradigmatic shift for the M&E industry. These trends include the rise of direct-to-consumer, content wars via mergers and acquisitions, and wavering audience loyalty. Change is ever-present, and the consequences of not adapting to the modern world became obvious and unavoidable in the face of the pandemic. Ultimately, M&E incumbents who are slow to modernize their technology, production, and monetization strategies will be left behind by more agile competitors

How M&E Companies Can Use the Cloud to Innovate

As we return “back to normal,” we’ll see how the pandemic affected our societal structures temporarily and permanently. The M&E industry was particularly changed in an irrevocable manner: a new age of media has been fully realized, and M&E businesses will have to rethink their business models as a result. How the pandemic will continue to evolve from here is still unknown, but it is clear that media organizations will have to continue to innovate in order to keep up with the changes in working patterns and audience behavior.

To adapt to the accelerated changes driven by COVID-19, the modern media supply chain will require agility, flexibility, and scalability. Cloud solutions (such as Microsoft Azure, Amazon Web Services, and Google Cloud Platform) are the key enabler for M&E companies as they look to innovate. According to a Gartner report on digital transformation in media and entertainment, 80% of broadcasters and content creators migrated all or part of their operations to public cloud platforms as an urgent response to effects of quarantine in 2020. By switching to cloud-based infrastructures, M&E companies were able to collaborate and create remotely, better understand real-time audience behavior, and maintain a secure environment while supporting media production, storage, processing, and distribution requirements.

There is no one-size-fits-all cloud strategy, as it is dependent on the business. Some companies opt for a single cloud provider, while others choose a multi cloud strategy. A hybrid cloud solution is also an option, which utilizes data centers in conjunction with cloud service providers. Regardless of a company’s cloud strategy, the benefits of migrating to the cloud remain the same. Below we’ll dive into a couple of the pros of utilizing the cloud for morderning workflows, supply chains, and data analyses. 

Unifying Workflows

With a cloud platform, teams can now collaborate remotely and globally, which ultimately leads to greater productivity and efficiency in content creation. When it comes to media production, whether it is live or pre-filmed, massive teams of professionals are needed to make the vision come alive (editors, visual effects artists, production professionals, etc.) COVID-19 demonstrated that teams using cloud service providers could still work collaboratively and effectively in a remote environment. In fact, businesses realized that requiring teams to come on-site for content production can be more time consuming and costly than working remotely. Virtual post-production is a great example of how the cloud is more economical from a financial and time sense. Using a modern cloud infrastructure, M&E brands can create virtual workstations, which replaces physical workstations at the user’s desk. Unlike traditional workstations, virtual workstations do not have a capital expense. Virtual workstations are extremely customizable in terms of size and power to the exact specifications needed for a given task. Furthermore, the billing is flexible and you only pay for what resources you use. Lastly, with physical workstations, there are many “hidden costs.” Think about the electricity and staffing fees that businesses must pay in order to keep a workstation running. When you switch to a virtual workstation for post-production work, all of the aforementioned costs are managed by a cloud service provider.

Streamlining the Media Supply Chain

As media and entertainment shifts to direct-to-consumer, content management has become absolutely crucial in the media supply chain. Content libraries are only growing bigger and there is an influx of newly-produced assets as team workflows work more efficiently. Even so, most media companies store their library assets on-premise and within tape-based LTO cartridges. By doing so, these assets are neither indexable, searchable, or readily accessible. This slows down editing, versioning, compliance checking, and repackaging, all of which hurts an organization’s ability for rapid content monetization. By implementing a cloud-based infrastructure, M&E companies can utilize tools like machine learning capabilities to manage, activate, and monetize their assets throughout the content supply chain.

Capturing Real-time Data

Archaic and lagged metrics, such as overnight ratings and box office returns, will struggle today to produce actionable insights. Digital transformation for M&E organizations will require a technology and cultural transformation towards a data-driven mindset. To make data-driven decisions, you need to have the tools to collect, process, and analyze the data. Cloud platforms can help process big data by employing machine learning capabilities to deeply understand audiences, which can translate into monetization opportunities further down the funnel. By harnessing the cloud to redefine data strategy, businesses can make confident decisions using real-time data and use actionable insights to deliver real transformation. 

Conclusion 

Before the pandemic, 2020 was shaping up to be a pivotal year for the M&E industry as audience behavior was changing and new competitors were cropping up; however, the effects of the COVID-19 expedited these trends and forced organizations to transform immediately. In this new age of media, M&E companies must reckon with these unique and long-lasting challenges and seek to change their business models, cultures and technologies to keep up with the changing landscape. 

-Anthony Torabi, Media & Entertainment Strategic Account Executive

Maximizing Cloud Data with Google Cloud Platform Services

If you’re trying to run your business smarter, not harder, utilizing data to gain insights into decision making gives you a competitive advantage. Cloud data offerings empower utilization of data in the cloud, and the Google Cloud Platform (GCP) is full of options. Whether you’re migrating data, upgrading to enterprise-class databases, or transforming customer experience on cloud-native databases – Google Cloud services can fit your needs.

Highlighting some of what Google has to offer

With so many data offerings from GCP, it’s nearly impossible to summarize them all. Some are open source projects being distributed by other vendors, while others were organically created by Google to service their own needs before being externalized to customers. A few of the most popular and widely used include the following.

  • BigQuery: Core to GCP, this serverless, scalable, multi-cloud, data warehouse enables business agility – including data manipulation and data transformation, and it is the engine for AI, machine learning (ML), and forecasting.
  • Cloud SQL: Traditional relational database in the cloud that reduces maintenance costs with fully managed services for MySQL, PostgreSQL, and SQL Server.
  • Spanner: Another fully managed relational database offering unlimited scale, consistency, and almost 100% availability – ideal for supply chain and inventory management across regions and between two databases.
  • Bigtable: Low latency, NoSQL, fully managed database for ML and forecasting, using very large amounts of data in analytical and operational workloads.
  • Data Fusion: Fully managed, cloud-native data integration tool that enables you to move different data sources to different targets – includes over 150 preconfigured connectors and transformers.
  • Firestore: From the Firebase world comes the next generation of Datastore. This cloud-native, NoSQL, document database lets you develop custom apps that directly connect to the database in real-time.
  • Cloud Storage: Object based storage can be considered a database because of all the things you can do with BigQuery – including using standard SQL language to query objects in storage.

Why BigQuery?

After more than 10 years of development, BigQuery has become a foundational data management tool for thousands of businesses. With a large ecosystem of integration partners and a powerful engine that shards queries across petabytes of data and delivers a response in seconds, there are many reasons BigQuery has stood the test of time. It’s more than just super speed, data availability, and insights.

Standard SQL language
If you know SQL, you know BigQuery. As a fully managed platform, it’s easy to learn and use. Simply populate the data and that’s it! You can also bring in large public datasets to experiment and further learn within the platform.

Front-end data
If you don’t have Looker, Tableau, or another type of business intelligence (BI) tool to visualize dashboards off of BigQuery, you can use the software development kit (SDK) for web-based front-end data display. For example, government health agencies can show the public real-time COVID-19 case numbers as they’re being reported. The ecosystem of BigQuery is so broad that it’s a source of truth for your reports, dashboards, and external data representations.

Analogous across offerings

Coming from on-prem, you may be pulling data into multiple platforms – BigQuery being one of them. GCP offerings have a similar interface and easy navigation, so functionality, user experience, and even endpoint verbs are the same. Easily manage different types of data based on the platforms and tools that deliver the most value.

BigQuery Omni

One of the latest GCP services was built with a similar API and platform console to various other platforms. The compatibility enables you to query data living in other places using standard SQL. With BigQuery Omni, you can connect and combine data from outside GCP without having to learn a new language.

Ready for the next step in your cloud journey?

As a Google Cloud Partner, 2nd Watch is here to be your trusted cloud advisor throughout your cloud data journey, empowering you to fuel business growth while reducing cloud complexity. Whether you’re embracing cloud data for the first time or finding new opportunities and solutions with AI, ML, and data science our team of data scientists can help. Contact Us for a targeted consultation and explore our full suite of advanced capabilities.

Learn more

Webinar: 6 Essential Tactics for your Data & Analytics Strategy

Webinar:  Building an ML foundation for Google BigQuery ML & Looker

-Sam Tawfik, Sr Product Marketing Manager

Cloud Crunch Podcast: 5 Strategies to Maximize Your Cloud’s Value – Create Competitive Advantage from your Data

AWS Data Expert, Saunak Chandra, joins today’s episode to break down the first of five strategies used to maximize your cloud’s value – creating competitive advantage from your data. We look at tactics including Amazon Redshift, RA3 node type, best practices for performance, data warehouses, and varying data structures. Listen now on Spotify, iTunes, iHeart Radio, Stitcher, or wherever you get your podcasts.

We’d love to hear from you! Email us at CloudCrunch@2ndwatch.com with comments, questions and ideas.