Category: Real-time Data

Top 10 Data Integration Tools for 2023

Posted on by Eyal Katz

Data is like breathing to your organization, especially if you’re pushing toward digital transformation and data-backed decision-making. Statista estimates the world will produce over 180 zettabytes of data by 2025. That’s a lot of data! So, what’s the challenge for organizations? Data is scattered across multiple sources, and integrating your data into a single place is a labor-intensive and time-consuming task.

That’s where data integration tools come in. It provides a centralized, efficient, and scalable solution for combining data into a single, unified view. In addition, it has handy additional features like automating manual tasks, improving data quality, enhancing security, and more.

This article will discuss everything you need to know about data integration tools, including the types, features, benefits, and ten different data integration tools you should know about.

Jump to…

data integration graph

What are data integration tools?

A data integration tool is a software system that combines data from multiple sources into a unified view. It automates the process of extracting, transforming, and loading data from different sources, including databases, applications, and cloud services. In addition, data integration platforms include features such as data mapping, data validation, data transformation, and data warehousing, as well as security and management tools to help ensure the quality and integrity of the integrated data.

Types of Data Integration Tools

  • Extract, transform, load (ETL) platforms: Used to extract data from source systems, transform them into a common format, and load it into a data warehouse or data lake.
  • Enterprise application integration (EAI) platforms: Designed to integrate business applications such as ERP and CRM systems.
  • Cloud integration platforms: Cloud integration platforms integrate data from cloud-based applications and services, such as Salesforce, Amazon Web Services (AWS), and Microsoft Azure.
  • Real-time integration platforms: Used to integrate data in real-time for time-sensitive applications such as financial services and e-commerce.
  • Change data capture (CDC) platforms: CDC platforms capture and replicate changes to data in real-time.
  • Master data management (MDM) platforms: Used to manage and maintain a consistent view of critical business data, such as customer and product information.

10 Benefits of Using a Data Integration Tools

  1. Improved data quality
  2. Increased efficiency
  3. Enhanced decision-making
  4. Real-time insights
  5. Improved data security
  6. Scalability
  7. Reduced costs through automation
  8. Improved data governance
  9. Better collaboration
  10. Increased agility

Key Features to Look For in a Data Integration Tool

When evaluating data integration tools, it’s important to consider the following key features:

  • Data connectivity: Linking to various data sources, such as on-premises systems, cloud apps, and databases.
  • Data transformation: Including tools for transforming and cleaning data, such as mapping, filtering, and normalizing, to ensure that data is accurate and consistent.
  • Data integration: Supporting real-time data integration and offering a centralized view of data from several sources.
  • Scalability: Handling large amounts of data and supporting the organization’s increasing data integration needs as it grows.
  • Security: Providing secure data management, including controlling access to sensitive data and ensuring that data is protected against unauthorized access and breaches.

Top 12 Data Integration Tools For 2023

1. Equalum 

equalum solution

Equalum is a data integration and orchestration tool designed to help organizations automate and streamline their data processing and management workflows. It offers a unified platform for data ingestion, transformation, orchestration, and delivery, allowing businesses to combine data from various sources, format it as needed, and transport it in real-time to target systems.

Best for: Real-time, cloud-native data integrations.

Price: Offers a free trial, and the pricing model is based on per feature. 

Review: “The platform is a powerful data integration and replication tool that combines advanced technologies, such as Kafka and Spark, with CDC (change data capture) capabilities. It provides unique features and performance advantages, making it an ideal solution for data migration and replication.”

2. Talend

Talend solution

Talend is a data integration and data management solution that offers a range of tools for data integration, including ETL, data migration, data quality, and master data management. Some of these tools are available in the cloud and on-premises, allowing organizations to choose the deployment option that best meets their needs.

Best for: Flexible cloud or on-premises deployment options.

Price: A free basic option is available, and Talend data cloud integration will cost 1170 USD per month/user.

Review: “Talend Data Integration helps to collaborate between different services and helps in data ingestion from various sources like Azure, AWS, on Premise, etc.”

3. Hevo Data

Hevo data solution

Hevo Data is a cloud-based data integration platform that enables organizations to collect, process, and analyze data from various sources in real-time. Hevo supports a range of data sources, including databases, cloud applications, and APIs, and allows real-time data ingestion. 

Best for: Strong data security features, such as encryption and user management.

Price: A free version with limited monthly events and basic features, a starter version for $239 per month with more connectors and support, and a custom-priced business version are available.

Review: “Hevo Data is a highly efficient and user-friendly data integration platform designed to meet the modern needs of ETL, ELT, and Reverse ETL. This platform requires no coding and provides bi-directional data pipeline capabilities, making it easy for data teams to manage and automate organizational data flows.”

4. Fivetran

Fivetran solution

Fivetran is a cloud-based data integration tool that enables organizations to seamlessly integrate their data in real-time without requiring manual intervention. It supports many data sources, including databases, cloud apps, and APIs. 

Best for: Zero-maintenance architecture makes sure that integrations are always current.

Price: Offers four models, Starter, Starter Select, Standard, and Enterprise, with custom pricing.

Review: “Fivetran provides pre-built connectors for over 100 data sources, with databases, SaaS applications, cloud storage, and more. I can’t not mention the Automated Data Pipelines. It means Fivetran automatically ingests, transforms, and loads data into the data warehouse, eliminating the need for manual data integration.”

5. StreamSets

Streamsets solution

StreamSets is an open-source data integration platform that provides a range of features for collecting, processing, and delivering data from various sources to various destinations. Developers and data engineers can easily create, manage, and monitor data pipelines with its user-friendly interface, which is meant to be extremely scalable and effective. 

Best for: DataOps with data integration.

Price: A free trial, a professional package for 1000 USD per month, and an enterprise package with custom pricing.

Review: “The platform is optimized for DataOps, improving team collaboration and efficiency. It supports modern analytics and hybrid integration, providing organizations with the ability to leverage their data for better decision-making.”

6. Aggua

Aggua solution

While not strictly a data integration platform, Aggua is a cloud-based data management platform that provides tools for data integration, quality, and governance. It helps organizations connect to and integrate data from various sources, ensure data accuracy and consistency, and manage data lineage and metadata. The platform has a user-friendly interface for working with data and includes collaboration and data-sharing tools.

Best for: Teams with Snowflake or BigQuery at the heart of their architecture.

Price: Contact the vendor for pricing plans.

Review: “It’s a tool for everyone on the data team and even for business analysts that are not part of the data team. I think I would frame it as a tool for everyone that is related to data somehow in the organization.”

7. Oracle GoldenGate

Oracle

Oracle GoldenGate is a real-time data integration and replication software solution. It provides real-time data integration and replication capabilities for organizations looking to consolidate data from multiple sources into a centralized data repository for analysis and reporting.

Best for: Streaming across multiple databases and platforms.

Price: A free trial and a license model at 250 USD.

Review: “With its ability to efficiently synchronize data between multiple systems, Oracle Golden Gate helps organizations keep their data up-to-date and ensures that critical information is always available when needed.”

8. Brtye

Bryte solution

Bryte is a data integration and management platform that assists businesses in streamlining workflows, automating data management processes, and enhancing the accuracy and quality of their data. Furthermore, it offers a range of data quality tools, including data profiling, data matching, and data standardization, to help organizations improve the accuracy and consistency of their data.

Best for: Automated data integration in real-time.

Price: BryteFlow pricing plans change based on the cloud service provider.

Review: “BryteFlow Ingest’s CDC capability for working with enterprise relational databases (e.g. MS SQL and Oracle) at scale and with operational stability, particularly with very large data volumes.”

9. Matillion

Matillion Solution

Matillion is a cloud-based data transformation and integration platform that enables organizations to extract, transform, and load data from various sources into cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery. In addition, the platform provides a visual interface for building and executing data integration jobs, making it easy for users with little to no coding experience to work with big data.

Best for: Perform ETL tasks for large amounts of data in cloud data warehouses for further analysis and reporting.

Price: Matillion Credits are the form of payment for the usage of Matillion ETL. They cost 2.00 USD per credit. Matillion offers a free version and three pricing plans: Basic, Advanced, and Enterprise, which start at 2.00, 2.20, and 2.30 credits.

Review: “It is optimized for cloud data platforms such as Snowflake, Delta Lake on Databricks, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse, allowing for efficient and productive data management across organizations. With its easy-to-use interface, Matillion makes it simple to make the world’s data useful. “

10. Striim

striim solution

Striim is a real-time data integration and streaming analytics platform for collecting, processing, and analyzing large volumes of data from various sources. It offers a scalable, fault-tolerant, and secure platform for data integration. 

Best for: Streaming analytics.

Price: A free version is available for 10 million monthly events, and there are two plans starting from 2500 USD per month.

Review: “Striim is a real-time data preparation tool that handles data streaming. It is equipped with intelligent modules, automation, and scripts that enable accurate and fast data ingestion.”

Equalum: Streamlining Your Data Journey

Using data integration tools not only improves the efficiency of the data integration process but also enhances the overall data quality and security.

With Equalum’s built-in CDC capabilities, you can further improve the data integration process by continuously accessing real-time data, tracking changes, and applying transformations before ETL. Find more on how Equalum can support your organization by booking a free demo.

7 Essential Practices For Working with BigQuery Datasets

Posted on by Eyal Katz

Google BigQuery is a modern, cloud-based data warehouse designed to augment the data handling capabilities of Big Data management systems. With very high data storage and processing capacity, it easily eclipses the power of traditional data warehouses for running complex analytical workloads. 

When dealing with Big Data, companies are forever playing the catchup game. The combination of velocity and volume makes it difficult to predict future data handling capacity for enterprise IT infrastructure. With over 36% of IT decision-makers facing this reality, it is a real problem. Google realized this early on and thankfully built BigQuery. 

In this post, we will explore the unique capabilities of Google BigQuery and identify the best practices for integrating it within the enterprise Big Data workflow.

What is BigQuery?

Initially released in 2010, Google BigQuery is a serverless data warehousing platform. It is highly scalable and can handle data in multiples of petabytes. It is also performant, with a highly parallel architecture that delivers very fast query responses. As a result, it is a superior alternative to traditional data warehouses.

There are essentially four phases to the typical workflow of a Big Data pipeline:

Big Data Pipeline

With traditional data warehouses, the processing and analysis phase causes a major bottleneck when the ingested data soars beyond a certain limit. Google BigQuery expedites these phases so that the data is processed with little overhead.

In Google BigQuery, data is organized within a top-level container known as the BigQuery Dataset. Within a BigQuery Dataset, the data is arranged in tables. Data from different tables can be logically combined into views for easier querying.

What public datasets does BigQuery support?

Google BigQuery hosts a few important public datasets, made available through the Google Cloud Public Dataset Program to foster innovation around data.

Some of the notable datasets made available through this program are patents, crime, and COVID-19-related data, and maps. These datasets can be searched in the Google Cloud marketplace and exported to the Google BigQuery console after signing in.    

What are the benefits of using BigQuery datasets?

One characteristic of Big Data, apart from volume and velocity, is the variety and veracity of data. Variety is because of different structuring, resulting in structured, semi-structured, and unstructured data, interspersed across data sources. Veracity in Big Data is caused due to anomalies in raw data or inconsistencies in processed data, causing duplicates, errors, or other forms of abnormalities. 

A Google BigQuery dataset supports all types of data structuring. Therefore, structured data as tables, and semi-structured or unstructured data in the form of CSV, JSON, and other formats, can be stored in the same dataset, and combined into views and queried together. As a result, data engineers do not have to set up separate pipelines for handling each structural type of data.

Additionally, Google BigQuery provides fine-grained control over a dataset, down to the column and row level. This mechanism ensures a single source of truth. It also alleviates the need for an additional wrapper of tools for data governance around the datasets to trace veracity issues in data.

With the rise of Artificial Intelligence (AI), Big Data pipelines are expected to perform data pre-processing tasks. 

Rather than setting up a separate ETL pipeline, Google BigQuery enables data scientists and data analysts to build and operationalize ML models right within the dataset.

What are the key features of BigQuery?

Besides its resilience in handling Big Data, Google BigQuery also offers some significant features that make it worth leveraging.

Google BigQuery natively supports standard SQL. This means that the data engineering teams have a familiar and well-known query language to work with the datasets. The SQL dialect is ANSI 2011 compliant and supports additional constructs for building and working with ML models.

Google BigQuery has built-in support for streaming data analytics. Streaming data can be ingested via BigQuery Streaming API, providing a low-latency, high-throughput access to the datasets. In addition, it also supports third-party streaming services and Pub/Sub messaging platforms. The built-in query acceleration ensures that the ingested streaming data is immediately available for querying in real-time.

Apart from these features, BigQuery also has native support for geospatial data. With this feature, data teams can perform analytics with spatial data to build location intelligence. They can also explore newer ways of presenting analytics reports within the context of geospatial data.

7 Essential Practices for Working with BigQuery Datasets

Google BigQuery is a great choice for developers and DataOps teams. Thanks to its free-tier options, on-demand availability, and flexible pricing, it is quite easy to get started with BigQuery.

However, Google BigQuery is a hosted platform and not the usual open-source tool that someone can spin off in a local environment. Consequently, working with Google BigQuery datasets requires some restraint and discipline. 

Here are the seven vital practices to make the most of Google BigQuery datasets, in terms of practicality, performance, and price.

1. Optimize queries for column-based access

Google BigQuery is a columnar database. The data within the datasets are all stored separately in columns, instead of rows. As a result, it is always advisable to run queries with column names, instead of using the standard wildcard ‘*” for selecting all columns. For example, this query returns the publication_number column from the patents.publications dataset:

SELECT publication_number FROM `patents-public-data.patents.publications` LIMIT 10 

Whereas, this query returns all the columns, which will make the query response size many times more than the data returned in the former case:

SELECT * FROM `patents-public-data.patents.publications` LIMIT 10

Additionally, dividing the tables in a dataset based on partitions and clusters can reduce the querying time and increase the performance.

It is important to remember that every query sent to Google BigQuery and every query response returned from it gets metered. Hence, care must be taken to prune the queries to limit the columns in the query response. This saves a lot of costs. Similarly, partitioning the table increases the query performance, which saves time.

2. Optimize the queries for Machine Learning (ML)

Google BigQuery datasets support direct machine learning interventions. By leveraging BigQuery ML, a built-in machine learning tool in BigQuery, data scientists can create and train ML models without the need to move data to a separate machine learning environment.

Using the same BigQuery Dataset, the data can be split into training, validation, and test sets, to train the model on one set, tune the hyperparameters on another set, and test the performance of the model on a third set. All of this is possible with custom SQL keywords for building and executing ML models. This has direct time and cost savings.

3. Configure change data capture (CDC)

Google BigQuery supports many options for data ingestion. Batch loading is a suitable choice for exporting a table as a first-time operation. However, for ingesting data as part of subsequent data updates, batch processing is inefficient.

Production data pipelines and real-time analytics jobs are better served through CDC technology. It captures the data updates from the data source, as it happens, with minimum latency.

Google BigQuery supports CDC. It also integrates with third-party data integration solution providers that facilitate better CDC orchestration with multiple data sources.

4. Maximize the analytics outcome

Google BigQuery is purpose-built for analytics. However, certain analytics tasks involve repeated data wrangling operations to access the same data. Much like query performance, the analytics performance must also be optimized for saving time and costs for such repeated operations. Here are a few ways: 

  1. Data schema: All tables must follow a data schema to ensure that data types and indexes are assigned appropriately. This is paramount for unstructured data, which is initially ingested as a table with a STRING data type. Before analytical processing, it is a must to transform such tables containing unstructured or semi-structured data.
  2. Materialized views: For specific analytics outputs that are accessed frequently, it is better to have materialized views of data. Materialized views are stored as physical subsets of the dataset and are faster to query compared to views.
  3. Query cache: BigQuery offers a caching feature. All query results are written to a table for instant access on the subsequent trigger of the same query. For analytics queries with very large response data, it pays to tweak the cache configuration for improving the repeat query execution time.

5. Watch over data security

BigQuery Datasets should always be secured for access control using Google Cloud Identity and Access Management (IAM). This is an often overlooked practice when starting out, but must be enforced, especially for securing the data used to train the ML models.

Even otherwise, from a data governance perspective, access control must be always in place such that permissions are granted only to those who need them. Similarly, all users must have access only to the data they need to perform their tasks.

6. Enable data lineage

Data lineage allows DataOps teams to trace data path from ingestion to consumption, recording all the actions taken along the way. In this way, it is possible to check for any alterations or transformations performed on data.

Google BigQuery recently added this feature, which is currently available in preview mode. Once enabled, a “Data Lineage” tab is available on the BigQuery console as a visual depiction. The recommended incorporation is into the audit workflow for easy exploration of data assets’ usage.

7. Always be monitoring the costs

Similar to other hosted platforms, Google BigQuery follows a pay-as-you-go model. Therefore, any increase in storage capacity and query operations runs the risk of cost overruns. Effectively managing the trade-offs between these two factors is the secret to controlling the costs. Accordingly, it is advisable to tune the BigQuery datasets based on a few options: 

  • Table expiration: Tables within a dataset can automatically be deleted after a certain period. This helps reduce storage costs by removing data that is no longer needed.
  • Storage tiers: BigQuery supports the concept of active and long-term storage tiers. The active tier is the default for accessing the dataset tables. The long-term storage tier is a lower-cost storage option for data that is accessed less frequently. This tier is designed for infrequently accessed or queried tables and is optimized for cost savings.
  • Flat-rate pricing: Google BigQuery reserves a certain capacity by choosing a flat-rate commitment. This can help reduce the cost of running queries that are run frequently or require a lot of computing resources. 

Apart from these options, all the query and analytics optimization practices mentioned above have an indirect contribution to cost savings. However, as an additional measure, Google BigQuery also offers budgets and quota limits configuration to keep a tab on costs.

Bringing BigQuery Closer to Data Sources with Equalum

Google BigQuery is a worthy solution to all the heavy lifting associated with the data pre-processing and ML training. It is also possible to augment its capabilities with in-flight ETL transformations. This approach is particularly helpful when dealing with streaming data.

By leveraging a data integration platform, such as Equalum, that has streaming ETL capabilities, data teams can build federated, real-time data streams from multiple data sources to Google BigQuery datasets. This is made possible with CDC. Additionally, with in-flight ETL, a virtual ETL pipeline can be built to enrich, aggregate, and cleanse the data before it reaches the BigQuery dataset. Equalum also enables you to bulk load to popular cloud data warehouses and lakes, helping you reduce costs while still improving the performance of the load and maintaining low latency. Get a free demo today.

Equalum’s CDC technology seamlessly integrates with DataOps and data engineers’ workflows by providing a low-code, visual interface to design their ETL pipelines. Along with its enterprise-grade reliability, Equalum is an ideal choice for building a future-proof data pipeline with Google BigQuery.

8 Essential Tips to Build a Big Data Pipeline that Scales

Posted on by Eyal Katz

It’s impossible to overstate the importance of data pipelines for modern organizations. These powerful tools enable businesses to extract, transform, and load large amounts of data from various sources, making it possible to process and move data quickly and efficiently. By building a data pipeline that scales and performs well, businesses can unlock valuable insights from their data and make informed, data-driven decisions.

However, building and maintaining a data pipeline can be complex and time-consuming, especially for organizations unfamiliar with the tools and technologies involved. In this article, we’ll explore the key considerations for building a data pipeline that scales, and we’ll offer tips and best practices for optimizing your pipeline to meet the changing needs of your organization.

What is a big data pipeline?

A big data pipeline is a subset of an ETL (extract, transform, load) solution used to process and move large amounts of structured and unstructured data from one place to another. It allows you to extract data from sources like databases, applications, and files and transform it into a format that can be easily analyzed and used by different systems or tools. 

There are two main big data pipeline architectures: real-time and batch streaming.
Real-time big data pipelines continuously collect and process data in near real-time, making it available for immediate use and analysis. This is useful for scenarios where you need to track and analyze data as it is generated, such as fraud detection or customer behavior analysis.

Data Pipeline Archiotecture

Batch streaming big data pipelines collect and process data in pre-determined intervals, typically in a time- or size-based manner. The ideal use case for batch streaming is in scenarios where you don’t need immediate access to the data, such as in data warehousing or reporting.

Big Data Use Cases

Big data pipelines can also be used in many industries, including government, education, transportation, energy and more. In general, organizations can use big data pipelines in any scenario where large amounts of data need to be processed, analyzed, and acted upon in real-time. Here are some use cases for big data:

Finance

A big data pipeline might be used to track and analyze market trends, such as stock prices, interest rates, and currency exchange rates. The finance industry collects this data from financial news websites, trading platforms, and regulatory agencies, then transforms and cleans it using transformation logic such as filtering, aggregation, and formatting. Finally, financial organizations load the data into a data warehouse or analytics platform, where it can be used to perform risk assessments, generate reports and comply with regulatory requirements.

Big Data Use Cases

Healthcare

A big data pipeline can positively impact the healthcare industry by analyzing patient data from multiple sources, such as electronic medical records, wearable devices, and clinical trials. Similarly to the finance industry, healthcare organizations clean the data using de-identification, data type conversion, and data cleansing techniques before they can use it to identify patterns and trends, predict outcomes and inform treatment decisions.

Retail

Retail businesses hugely benefit from big data pipelines. The data that the retail industry gathers from website traffic, social media, point-of-sale systems, and more can be used for sales, marketing, and business operations. For example, marketing teams using customer data might provide personalized shopper recommendations to optimize their campaigns. 

Manufacturing

Machine data like sensors and logs provide vital insights into optimizing production processes. The data can identify patterns and trends, predict maintenance needs, and optimize production schedules.

6 Key Components of a Data Pipeline

A typical big data pipeline consists of the following components:

  1. Data sources: The data, which can be structured, unstructured, or semi-structured. Examples of data sources include databases, files, logs, sensors, and APIs.
  2. Data connectors: The tools and technologies used to connect to and extract data from various sources. E.g., JDBC drivers, ODBC drivers, REST APIs, and file transfer protocols.
  3. Data transformation logic: The logic applied to the data to clean, filter, aggregate, and enrich it. You can implement this logic using a variety of programming languages, such as SQL, Python, or Java.
  4. Data pipeline runtime: The infrastructure that runs the data pipeline, such as a server, cluster, or cloud platform. The runtime can be on-premises or in the cloud, and it can be scaled up or down to meet the needs of the pipeline.
  5. Data targets: The destination of the data, which can be a data warehouse, data lake, analytics platform, or any other type of data storage or processing system.
  6. Data monitoring and management: The process of monitoring the performance and quality of the data pipeline, including error handling, debugging, and optimization. You can do this using dashboards, alerts, and logs.
Data Pipeline: Extract, Transform, Load

7 Essential Tips to Build Big Data Pipeline that Scales

1. Set Clear Goals and Requirements

Before building a big data pipeline, it is essential to clearly understand what you want to achieve and your specific requirements. Planning will help you design the pipeline to meet your needs and align with your business objectives.

2. Choose the Right Data Connectors

The data connectors you use are vital when building a big data pipeline. You can choose connectors that are reliable, scalable, and capable of handling the data sources and targets you are using. Taking the time to carefully consider your connector options upfront will pay off in the long run, helping you avoid headaches and setbacks down the line.

3. Use Transformation Logic Wisely

The transformation logic you use in your pipeline can greatly impact its performance and scalability. To select the right logic for the job, you might consider combining batch and stream processing to optimize performance. By carefully selecting your transformation logic and using it wisely, you can help ensure that your pipeline runs smoothly and efficiently.

4. Choose the Right Runtime Platform

When selecting a runtime platform for your big data pipeline, there are several factors to consider. You may need to weigh each option’s cost, performance, security, and availability.

5. Monitor and Optimize Your Pipeline

Continuous monitoring and optimization of your pipeline are key to ensuring it performs at its best. 

6. Secure Your Data

Security is central to building a big data pipeline, particularly when handling sensitive or regulated data. Implementing measures such as encryption, authentication, and access controls are essential to protect your data and ensure compliance. 

7. Consider a Helping Hand

Building and maintaining a big data pipeline can be complex and time-consuming, especially if you’re unfamiliar with the tools and technologies involved. One option to consider is using an end-to-end data integration and streaming platform, such as Equalum, to streamline the process and reduce the workload on your team. Managed solutions like Equalum offer a range of features and benefits, including CDC capabilities, real-time streaming, and scalability, to help you get the most out of your data. If your team is concerned that collecting, cleaning, and transforming large amounts of data will negatively affect productivity, Equalum might be able to give you a helping hand.

Build Scalable Big Data Pipelines For Future Business Growth

Whether you are a marketing team hoping to unlock customer insights, a finance company tracking stock fluctuations, or a retail business monitoring inventory levels, big data pipelines can help you gain real-time visibility over your data and maximize its business value. 

Don’t let the complexities of building and maintaining a big data pipeline hold you back. Let Equalum help you get the most out of your data.  Our advanced CDC capabilities enable real-time streaming and data replication in minutes, so you can ingest, transform, and deliver your data in real-time.
Try Equalum today and see for yourself.

When and Why Real-Time Data Matters

Posted on by Nir Livneh

Why Real-Time Data Matters for Your Business

Attendees at any big data or data science conference might very well leave believing that the future will be dominated entirely by the use of real-time data. Batch is dead. Long live real-time!

There’s certainly no shortage of heady optimism about the future of real-time data. But proponents haven’t always been rigorous in defining why and in what situations real-time data matters. As a result, some technology leaders have rightfully pushed back, questioning whether batch data and legacy ETL processes are “good enough.”

Real-Time Data can Improve Business Analytics and Operations

The reality is that the use of batch data for analysis and decision-making isn’t going away any time soon – because there is still a place for it. Architecting a streaming data solution in order to report on last month’s financial numbers would be unnecessary.

That being said, real-time data is a must for any application where the cost of data latency is high. Businesses think of the cost in many ways, but generally, it falls into the bucket of lost revenue (e.g., from customer churn or inventory shortages) or actual financial outlays (e.g., for equipment repair or security remediation).

Successful Customer Facing Experiences Often Require Real-Time Response

Here are a few situations where real-time data is critical – and what industry leaders are doing to take advantage of the opportunity afforded by real-time data technologies:

  • Customer workflows: Customer expectations for immediacy and personalization are rapidly changing, and businesses from retail to financial services are struggling to keep pace. The cost of data latency in customer-facing experiences can have serious consequences: irrelevance (leading to erosion in brand perception/loyalty) or friction in the customer journey (leading to drop-off and lower conversion). For example, a retailer serving a display advertisement for a product that a customer just purchased creates an alienating user experience. Similarly, a customer seeking an auto loan is likely to favor the bank with an instant loan review and approval process over the one that takes minutes or even hours.
    • Example: A leading media company correlates viewership and social media data to inform ad buying decisions in real-time – investing in the content and ad platforms that are most relevant to their viewing audiences.

Use Real-Time Data to Prevent Cost Escalations for Your Business

  • Cost containment: Real-time data can afford critical insights for preventing cost escalation. For example, real-time supply chain optimization can help companies predict and remediate critical inventory issues before shortages result in missed sales or require costly interventions. Similarly, industrial manufacturers may rely on real-time analysis of machine data to optimize preventative maintenance, preventing equipment damage that can be devastating to manufacturing output.
  • Health providers interpreting results from network-connected devices can detect anomalies in real-time, preventing patient health emergencies like strokes or heart attacks.
    • Example: A Fortune 100 industrial manufacturing company makes use of a digital twin to identify anomalies in the and optimize preventative maintenance on equipment.

Respond Quickly to Cybersecurity Threats using Real-Time Data to Detect Anomalies

  • Threat detection: The growth of cybersecurity threats has placed an increased premium on threat detection (including network, application, endpoint, cloud, and wireless security). While security breaches represent can result in staggering direct and indirect costs to businesses, response speed has a significant impact on the ultimate cost of the breach.
    • Example: leading financial institutions monitor network traffic in real-time in order to detect anomalies that could signal intrusion attempts.

Ultimately, real-time data can provide a critical edge that helps enterprises navigate today’s fast-paced business landscape.

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