The Benefits of MLOps: Streamlining Machine Learning Workflow and Deployment

The Benefits of MLOps: Streamlining Machine Learning Workflow and Deployment

by | Apr 12, 2023 | Business Intelligence

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Putting machine learning models into production can be long and complex. There is a large margin for error between the experimental phase and the production floor, and the success of the deployment is contingent on several moving parts in the data, machine learning model, and code. The MLOps approach can help simplify this communication. While MLOps takes inspiration from DevOps, it also has its unique characteristics.

What is MLOps?

Machine learning operations (MLOps) is a strategy for overseeing all aspects of the machine learning model’s lifecycle, from development to regular use in production to eventual retirement.

Machine learning ops, or “DevOps for ML,” is an initiative to strengthen the relationship between the data scientists who create machine learning models and the operations groups who monitor the models’ use in production. 

It is accomplished through enhancing feedback loops and automating as many routine procedures as possible.

MLOps’ primary objective is to facilitate the application of AI technologies to business challenges, with a secondary focus on assuring that the results of any machine learning (ML) models adhere to ethical and reliable standards.

Let’s have a look at the critical components of the MLOps technique.

1. Version control

DevOps relies heavily on stable and up-to-date code versions. The data and models developed in MLOps must also be versioned. There must also be a correlation between the various versioning procedures. 

Each model requires its unique collection of data and version of the software. 

2. ML Pipelines

All the steps needed to create a model are part of the ML pipeline.

  • Preparing Input Data
  • Model development and evaluation
  • Providing feedback on the models’ performance (metrics, plots)
  • Model code continuous integration/continuous deployment registration and deployment

    Modifications to the code, the introduction of new data, or a predetermined timetable can all set off the ML pipeline. It is possible to create non-linear, complex workflows. 

3. Monitoring

As machine learning relies on recursive mathematical functions rather than hardcoded instructions, it is not uncommon for an ML model’s performance to degrade over time as new data is added. 

It is essential to keep an eye on this phenomenon, known as model drift, to ensure the model’s results stay within acceptable ranges.

4. Collaboration

Effective ML deployments necessitate a wide range of technical expertise and an organizational culture prioritizing teamwork across departments. 

Data scientists developing machine learning models and operations teams maintaining them in production can close the cultural and technological gaps through feedback loops.

5. Cloud computing environment

Training the model in the cloud creates a centralized, or at least shared, environment, which is excellent for teamwork among data scientists. 

Uniformity and repeatability, two factors essential to the timely completion of a machine learning project, are provided by a centralized setting and automated procedures.

For Data Nectar’s cloud-based code repository, we usually use a DevOps platform. Instead of keeping the trained model on our servers, we save it in a central registry just for models. The information can be held in the cloud and retrieved/mounted at the start of the training process. 

MLOps Deployment

An effective MLOps implementation can serve as a monitoring and automation system for ML models from their inception until retirement. 

Data scientists, programmers, compliance teams, data engineers, ML researchers, and company executives will all benefit from MLOps at its peak performance. 

There is a high failure rate with MLOps if it is not implemented correctly. Cultural issues, such as conflicting priorities and poor communication between departments, are a typical source of problems in organizations.

As a result, new feedback loops and technical features of a model’s lifecycle support services and tools are widely embraced.

What are the most effective MLOps practices?

Teams working on MLOps tools involve people from various parts of the business. 

Maintaining open lines of communication and adhering to best practices for each component of the pipeline can help data scientists, engineers, analysts, operations, and other stakeholders create and deploy ML models that consistently produce the best possible outcomes. Some examples are:

1. Data Preparation

One of the most crucial aspects of any ML model is the quality of the data. It is critical that the data used to train ML models is properly preprocessed. This entails the most effective methods for data cleansing, data exploration, and data transformation.

2. Engineered Features

Improving the precision of supervised learning results is a primary motivation for feature engineering. The process of data verification is an example of best practice. Making ensuring feature extraction scripts can be utilized again in production for retraining is also crucial.

3. Data Labeling

Superior label quality is essential for supervised learning. Best practices include having a clearly defined and reviewed labeling procedure that is accepted by experts in the field. 

4. Practice and Adjustment

Train and tweak simple, interpretable ML models first since they are simpler to debug. 

The debugging process for more sophisticated models can be simplified with the use of the following ML toolkits 

  • Google Cloud AutoML, 
  • MLflow, 
  • Scikit-Learn
  • Microsoft Azure ML Studio.

5. Auditing and Managing

Best practices for MLOps include version control, just as they do for DevOps. One way to check for modifications made to a model over its lifetime is to trace its ancestry. 

This best practice can be bolstered by utilizing cloud platforms like MLflow or Amazon SageMaker.

6. Monitoring

Monitoring the model’s outputs and summary data on a regular basis after deployment is an essential best practice. This entails keeping a watch on the following

  • Measures taken to check the load, utilization, storage, and health of the infrastructure on which the model runs are known as benchmarks.
  • Over- or under-representation in the incoming data might induce a bias that is summarized statistically. 
  • The core of the ML model. When a model’s outputs deviate outside of predetermined statistical thresholds, an automatic alert system can initiate the retraining process.

Advantages of MLOps

1. Lifecycle speed

Machine Learning ops (MLOps) is a defined procedure for developing reusable pipelines for machine learning. 

As opposed to the months-long process of unplanned coordination between the various specialist teams involved in a project, a machine learning model can proceed quickly from inspiration to deployment.

2. Convenient Reproduction system

Reproducing models is considerably simpler now because your code, data, and all previous versions of both are stored in the cloud. 

Data scientists can reliably recreate their models locally or in the cloud whenever they need to make adjustments.

3. Highly Collaborating

Machine learning tools development often calls for interdisciplinary groups to work together, with members having backgrounds in things like information technology, dev ops engineering, and data science. 

In the absence of a development framework that promotes teamwork, individuals tend to work in isolation, leading to inefficient delays that eat up resources. 

As part of the MLOps procedure, groups must work together to orchestrate their operations, shortening development timeframes and producing a product more suited to the business goal.

4. Standardization of data governance and regulatory compliance

There has been a tightening of rules and guidelines in the machine learning sector as the technology has matured.  

The General Data Protection Regulation (GDPR) of the European Union and the California Privacy Rights Act (CPRA) are two examples of regulations that could affect the use of machine learning. 

Machine learning models that are in line with applicable governmental and industry requirements can be replicated using the MLOps procedure.

5. Scalability

Using MLOps practices, which emphasize standardization, helps businesses swiftly increase the amount of machine learning pipelines they construct, manage, and monitor without significantly increasing their teams of data experts. 

Hence, MLOps allows ML projects to scale very well.

6. Components and models that can be reused

It is simple to adapt MLOps-built machine learning features and models to serve alternative organizational goals. 

The time it takes to deploy is cut even further by reusing pre-existing components and models, allowing for the rapid achievement of meaningful business goals.

Final Words

MLOps helps teams to bring their machine learning applications into a production setting much more quickly and with better outcomes. Teams can more easily adjust to new circumstances if they have a well-defined deployment strategy in place that links the staging and production environments. And since everything (data, models, and code) is versioned meticulously in the cloud, you never have to worry about losing any of your hard work.

While the controlled development process that MLOps provides may involve some growing pains for your team, we at Data-Nectar are firm believers in its merits. Do you need to get your team up to speed on MLOps? Please let us know how we can assist you.

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A look into Snowflake Data Types

A look into Snowflake Data Types

by | Feb 10, 2023 | Business Intelligence

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As a Database as a Service (DBaaS), Snowflake is a relational Cloud Data Warehouse that can be accessed online. This Data Warehouse can give your company more leeway to adapt to shifting market conditions and grow as needed. Its Cloud Storage is powerful enough to accommodate endless volumes of both structured and semi-structured data. As a result, information from numerous sources can be combined. In addition, the Snowflake Data Warehouse will prevent your company from needing to buy extra hardware.

Snowflake allows you to use the usual SQL data types in your columns, local variables, expressions, and parameters (with certain limitations). An identifier and data type will be assigned to each column in a table. The data type tells Snowflake how much space to set aside for a column’s storage and what form the data must take.

Snowflake’s great global success can be attributed to the following characteristics: 

    • Snowflake’s scalability stems from the fact that it provides storage facilities independent of its computation facilities. Data is stored in a database and processed in a virtual data warehouse. As a result, Snowflake guarantees excellent scalability at a low cost.
    • Snowflake requires little upkeep because it was made with the user in mind. It has a low barrier to entry and needs little in the way of upkeep.
    • Automated query optimization is supported in Snowflake, saving you time and effort over the hassle of improving queries manually.
    • Snowflake allows you to divide your company’s regular workloads into different virtual Data Warehouses. As a result, this facilitates Data Analytics management, particularly under extremely regular loads.

Six Important Snowflake Data Types

The first step in becoming a Snowflake Data Warehouse expert is learning the ins and outs of the different types of data it stores. There are 6 different kinds of data that can be used with Snowflake.

    1. Numeric Data Types
    2. String & Binary Data Types
    3. Logical Data Types
    4. Date & Time Data Types
    5. Semi-structured Data Types
    6. Geospatial Data Types

1) Numeric Data Types

Knowing what precision and scale are is crucial before diving into the various sorts of numeric data types. 

    • A number’s precision is the maximum number of significant digits that can be included in the number itself.
    • Scale is the maximum number of digits that can be displayed following a decimal point.

Precision has no effect on storage; for example, the same number in columns with different precisions, such as NUMBER(5,0) and NUMBER(25,0), will have the same storage requirements. However, the scale has an effect on storage; for example, the same data saved in a column of type NUMBER(20,5) requires more space than NUMBER(20,0). Additionally, processing bigger scale values may take a little more time and space in memory.

So here are a few types of numeric data types:

    • NUMBER is a data type for storing whole numbers. The default scale and precision settings are 0 and 38, respectively.
    • DECIMAL and NUMERIC are the same as NUMBER.
    • The prefixes INT, INTEGER, BIGINT, and SMALLINT all mean the same thing as NUMBER. But you can’t change the scale or precision; these serial data types are permanently stuck at 0 and 38.
    • Snowflake uses double-precision IEEE 754 floating-point values (FLOAT, FLOAT4, FLOAT8). 
    • FLOAT is a synonym for DOUBLE, DOUBLE PRECISION, and REAL.
    • Numeric Constants are numbers that have fixed values. It supports the following format:

2) String & Binary Data Types

The following character-related data types are supported in Snowflake:

    • With a maximum size of 16 MB, VARCHAR can store Unicode characters of any size. There are BI/ETL tools that can set the maximum allowed length of VARCHAR data before storing or retrieving it.
    • CHARACTER, CHAR is like  VARCHAR, but with the default length as VARCHAR(1).
    • If you’re familiar with VARCHAR, you’ll feel right at home with STRING.
    • Just like VARCHAR, TEXT can store any kind of character.
    • The BINARY data type does not understand Unicode characters; hence its size is always expressed in bytes rather than characters. There’s an upper limit of 8 MB.
    • To put it simply, VARBINARY is another name for BINARY.
    • String Constants are fixed values. When using Snowflake, string constants must always be separated by delimiter characters. Delimiting string literals in Snowflake can be done with either single quotes or dollar signs.

3) Logical Data Types

In logical data type, you can only use BOOLEAN with one of two values: TRUE or FALSE. Sometimes it will show up as NULL if the value is unknown. The BOOLEAN data type offers the necessary Ternary Logic functionality.

SQL requires using a ternary logic, often known as three-valued logic (3VL), which has three possible truth values (TRUE, FALSE, and UNKNOWN). To indicate the unknown value in Snowflake, NULL is used. The outcomes of logical operations like AND, OR, and NOT are affected by ternary logic when applied to the evaluation of Boolean expressions and predicates.

    • UNKNOWN values are interpreted as NULL when used in expressions (like a SELECT list).
    • Use of UNKNOWN as a predicate (in a WHERE clause, for example) always returns FALSE

4) Date & Time Data Types

This details the date/time and time data types that can be managed in Snowflake. It also explains the allowed formats for string constants to manipulate dates, times, and timestamps.

    • The DATE data type is supported in Snowflake (with no time elements). It supports the most typical dates format (YYYY-MM-DD, DD-MON-YYYY, etc.).
    • DATETIME is shorthand for TIMESTAMP NTZ.
    • A TIME data type represented as HH:MM: SS is supported by Snowflake. Additionally, a precision setting for fractional seconds is available. The default precision is 9. The valid range for All-TIME values is between 00:00:00 to 23:59:59.999999999. 
    • An alternative name for any of the TIMESTAMP_* functions is TIMESTAMP, which can be set by the user. The TIMESTAMP_* variant is used in place of TIMESTAMP whenever possible. This data type is not stored in tables.
    • Snowflake supports three different timestamp formats: TIMESTAMP LTZ, TIMESTAMP NTZ, and TIMESTAMP TZ.

       

      • The TIMESTAMP LTZ function accurately records UTC. The TIMEZONE session parameter determines the time zone in which each operation is executed.
      • TIMESTAMP NTZ accurately records wallclock time. Without regard to local time, all tasks are carried out.
      • By default, TIMESTAMP TZ stores UTC time plus the appropriate time zone offset. The session time zone offset will be utilized if the time zone is not specified.

5) Semi-Structured Data Types

Semi-structured data formats, such as JSON, Avro, ORC, Parquet, or XML, stand in for free-form data structures and are used to load and process data. To maximize performance and efficiency, Snowflake stores these in a compressed columnar binary representation internally.

    • VARIANT is a generic data type that can hold information of any other type, including OBJECT and ARRAY. Its 16 MB of storage space makes it perfect for archiving large files.
    • OBJECT comes in handy to save collections of key-value pairs, where the key is always a non-empty string and the value is always a VARIANT. Explicitly-typed objects are currently not supported in Snowflake.
    • Display both sparse and dense arrays of any size with ARRAY. The values are of the VARIANT type, and indices can be any positive integer up to 2^31-1. Arrays of a fixed size or containing values of a non-VARIANT type are not currently supported in Snowflake.

6) Geospatial Data Types

Snowflake has built-in support for geographic elements like points, lines, and polygons. The GEOGRAPHY data type, which Snowflake provides, treats Earth as though it were a perfect sphere. It is aligned with WGS 84 standards.

Degrees of longitude (from -180 to +180) and latitude (from -90 to +90) are used to locate points on Earth’s surface. As of right now, altitude is not a supported option.  More so, Snowflake provides GEOGRAPHY data-type-specific geographic functions.

Instead of retaining geographical data in their native formats in VARCHAR, VARIANT, or NUMBER columns, you should transform and save this data in GEOGRAPHY columns. The efficiency of geographical queries can be greatly enhanced if data is stored in GEOGRAPHY columns.

The following geospatial objects are compatible with the GEOGRAPHY data type:

    • Point
    • MultiPoint
    • MultiLineString
    • LineString
    • GeometryCollection
    • Polygon
    • MultiPolygon
    • Feature
    • FeatureCollection

Unsupported Data Types

If the above list of SQL server data types is clear, then what is the type of data that is incompatible with Snowflake? Here is your answer.

  • LOB (Large Object) 
    • BLOB: You can also utilize BINARY, with a maximum size of 8,388,608 bytes. 
    • CLOB: You can also use VARCHAR, with a maximum size of 16,777,216 bytes (for a single byte).
  • Other
    • ENUM
    • User-defined data types

Conclusion

While your primary focus should be on learning how to use customer data, you may be questioning why it’s necessary to know so many different data types. There is one motive for doing this, and that is to amass reliable information. Data collection and instrumentation aren’t the only areas where you can use your data type knowledge; you’ll also find that data administration, data integration, and developing internal applications are much less of a challenge now that you have a firm grasp on the topic.

Also, without a good database management system, it is impossible to deal with the massive amounts of data already in existence. Get in touch with our experts for more information.

ETL, or ELT? Which is better? What’s the difference?

ETL, or ELT? Which is better? What’s the difference?

by | Feb 9, 2023 | Business Intelligence

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Background – The uncontrollable surge of data

The web is getting inundated with gigabytes of data every second and this data flood is continuously rising as if Moor’s law also applies to data as well as to the microprocessor’s performance. By 2025 the total annual consumption of data is estimated to cross 181 zettabytes compared to 97 zettabytes in 2022.

Almost every single digital device captures some kind of data and transmits it to a corresponding data collection center for processing, analyzing, synthesizing, and getting integrated with other relevant types of data to produce new information. The decision-making process in every organization now heavily relies upon data-derived knowledge as data patterns in various context reveals prevalent world trends. This helps the decision-makers spot opportunities for their next actions or even leads to innovation.

This has created an oversize cloud of data in the web space – structured, or unstructured in every format conceivable. From the outset, it looks like anarchical  chaos because the locations of these data formats keep changing, and one source point can collect multiple types of data. Such data alone as it is collected can not make any meaning. It has to be seen in its individual context to make sense.

For that, it requires the data to be integrated in specific manners. And special methodologies are employed to integrate such a large amount of data. At present, the industry has adopted two types of data integration strategies to streamline multiple disparate information into one stable source of knowledge. They are called ETL and ELT. Though the end result is expected to be the same through both of them, applied methods are the distinguishing factor.

First, let us understand what data integration is.

Data Integration

As it implies, it means ‘the process of integrating various types of data from various sources in a ‘unified way’ so that it can be used for the subsequent systems or for advanced analytics. Integrated data can be located at a central source for multiple users and stakeholders to derive optimum performance from their data operations such as queries, and minimize the load on the data source.

It’s used for analytical purposes, sending the results to databases, data warehouses, or data marts, and organizing the data in specific analytic formats like star schemas, multi-dimensional, etc. Data integration also includes enhancing the data inside an application such as CRM and bringing together all the data need for a new downstream application.

Data integration processes

Data integration involves three key processes

  • Extracting the data, 
  • Transforming it into a well-defined unified view, and 
  • Loading the unified data into the destination.

Very rarely all three operations consist of one single process. Most of the time all three steps run separately. The extraction is always the first process. But Loading, and transforming steps can be executed in two different orders. Therefore sometimes it is ETL, and sometimes ELT.

Both processes have their distinctive advantages. We’ll understand the difference between ELT and ETL later in this article. But first let’s define each method the E, the T, and the L.

Extraction

Raw data is pulled from a source or multiple sources such as transactional systems, CRM data, or ERP data, or IoT sensors that collect readings from various source points. The extracted data can be in multiple formats, such as CSV, relational databases, MS Access files, XML, JSON, etc. Typically it involves using SQL for extraction. Generally, the extracted data is in an unstructured format.

It’s an accepted practice in the ETL processes that the data is temporarily placed into a staging repository.

Transformation

In this process the schema is employed and the data is reorganized, reframed, reshaped, reordered, reconfigured, and updated to match the business requirements and data storage solution needs.

It can include filtering, pulling in data from other sources, and other processes, converting various data types into one single format, cleansing the data by removing inconsistencies and duplicate entries, validating, and authenticating.

The calculation, conversions of the raw data headers as required by the destination’s needs, converting of measuring units and currencies, as the BI process demands, analysis or synthesis, making it compliant with government regulations, etc also take place at this stage and also deleting inaccurate data raws (this can also take place during the extraction process,) blending, enrichment, aggregating or combining data elements from multiple data models.

During the process, the data is also run through a cleansing process to prevent the inclusion of bad or non-matching data in the destination repository. A set of predefined rules and functions are also applied.

Additional transformation also must be done to clean and model data before it can be practically useful for other analytics applications.

Loading

Loading is a process that delivers the transformed data into the destination data store such as a data warehouse, data mart, analytical database, or OLAP engine, and secures it for sharing (It is also a possibility that some of the final reorganizations of the data can be defined and coded during this step.)

This step makes the data business-ready and makes it available to internal and external users. The destination’s existing data can also get overwritten as a result of this process. This possibility can be handled in advance prior to executing the loading process.

With these three processes, the data integration can be conducted in two ways ELT, and ETL. In the ELT methodology, data is copied from the source directly into the data warehouse without much alteration. In the ETL methodology data is transformed into a suitable format before it’s loaded into the warehouse.

We will look into these processes in detail later in this article.

Similarities in both processes

  • Both ETL and ELT allow the consolidation of data from multiple sources into a single repository with data that is formatted in a predefined way. 
  • This single unified data repository makes simplified access for analysis and additional processing possible.
  • It also provides a single source of up-to-date knowledge ensuring the consistency of all enterprise data.

What is ETL? 

ETL is a data integration methodology in the order of Extracting, Transforming, and then Loading.

The raw data is first copied from the origin, then cleansed and restructured/reformatted in a ‘staging area’ during the transform phase, and then moved into the data warehouse or pushed forward.

The ETL workflow is linear in nature and it pairs well with relational data warehouses. It requires data transformations to enforce strict schema and data quality before loading to the destination.

At the same time, ELT is best compatible with data lakes that accept structured or unstructured data.

ETL Use cases

The ETL data integration is useful in these kinds of scenarios…

    • Where the data is being real-time extracted from the AI-enabled devices for machine learning.
    • To integrate and analyze the real-time influx of marketing data.
    • To integrate and process the IoT data collection.
    • Replication of multiple databases in real-time for more than one destination.
    • Real-time migration of the databases to the cloud.

When to use ETL:

The most common use cases of ETL software in the enterprise sector are Data Warehousing and Data Migration.

Data Warehousing.

A data warehouse is a database with data from multiple sources combined in a fashion that can be collectively analyzed as per the needs.

Data Warehouse is a well-organized environment where a large amount of database is housed. Prior to the loading of the data onto that, it is taken through many of the operations we talked about in one of the above sections, and transformed. Once it’s loaded, it becomes a ‘single point of reference for the business.

Quality, accuracy, and consistency are vital factors to build a data warehouse. The ETL tool does that whether it is employed on-premise, or in a cloud data warehouse so that the subsequent processes can be executed effortlessly.

Data Migration

ETL is also used to upgrade databases or transport data from a legacy system to a modern one.

The main challenge against data migration is a mismatching data structure and table format. The ETL tool can iron out these disparities with its modern enhanced abilities and ensures compatibility of the source data (the format, structure, and scheme) with the target system.

What is ELT?

This process is first to extract, then load the data, and transform. It is relatively a newer method for achieving the integration of data from across an organization and preventing uncontrolled data duplication.

Data is extracted from its origin, loaded into the datastore, and then taken through the pre-defined business logic to get transformed “as and when required.” The transformation will typically happen on an as-needed basis, while in the ETL methodology, all data is transformed first before it is stored.

ELT Use Cases

ELT is generally used in real-time high-volume data environments such as stock exchanges, large-scale distribution networks, industrial processes, and anybody who need real-time access to updated data and business intelligence for instant decision-making.

The most common use case of ELT software in the enterprise sector is…

When to use ELT?

Optimization of the pushdown process

In an ELT process, data is fetched, loaded onto the database, and then taken through the transformation processes. For high-volume data, this process is preferred. It prevents the server from getting overburdened because the processing is performed in the database.

ELT’s greatest strength is its speed and support for diverse data types.

Other Applications when ELT is a better choice

        • When data needs to be stored at high speed.
        • When the data integration process requires to be flexible and data needs frequent formatting.
        • When the amount of data to be processed is large.
        • When the raw historical data has to be retained for future analysis. If the business needs to analyze data trends, old data is required. ELT keeps old raw data and doesn’t require reloading.

Main differences between ETL vs ELT

The major difference between ETL and ELT is the order of the events through which data integration is accomplished.

ETL

  • Using ETL is a preferred option when on-premise data needs to be structured before being uploaded onto a data warehouse. 
  • This method works efficiently when datasets are not large because large datasets will consume more time and computing resources.
  • Also, the process and parameters are predefined for the transformation phase.

ELT

  • At the same time ELT is preferred when the data volume is large.
  • Also, it’s implemented in an environment where the storage is large and adequate computing power available. 
  • This allows the data lake to efficiently store and transform data as required. 
  • The format of data can also be modified at short notice in ELT however running of such queries can certainly add up that much time to the process. 
  • In ETL queries can be applied right away on the data after loading.

Comparison of the Benefits of ELT over ETL

Comparison parameter ETL ELT
Flexibility Intended for structured relational data Structured as well unstructured data allowed on cloud-based data warehouses
Accessibility Supported, maintained, and governed by the IT teams. Easy for the users to access and use.
Scalability Resource intensive, local, and limited availability. Cloud based SaaS, is available to a wide range of users.
Loading time Takes longer to load as it comes through the staging area for the transformation. As it copies raw data as it is onto the destination, it loads it quickly.
Transformation time is generally slower. Dependent on the size of the datasets. Not dependent on the data size.
Maintenance  Less time required. If the transformation doesn’t meet the business needs, the data has to be re-sourced and re-loaded. Additional transformation can be performed as the original data is intact and already loaded. 

ETL vs. ELT: Pros and Cons

It’s clearly context-dependent so there isn’t a verdict for which one is better than the other. Both data management methods have their distinctive advantages and shortcomings. Let us review them one by one.

Advantages of ELT

    1. More effective in a real-time data scenario.
    2. Preferred choice to handle large data volume or big data.
    3. Preferred when the transformation process is complex.
    4. Can work with schema-less data such as No-SQL, Mongo DB, and Cosmos DB.
    5. In terms of flexibility of data formats, ELT can ingest data in any format, even when paired with a data lake. The data lake even accepts unstructured data.
    6. The transformation can take place as and when needed. This can keep the source compute system free from large storage needs and heavy processing leaving the storage space available for other functions and doesn’t much consume the processing power.
    7. ELT loads all the data to the data lake making it available anytime. That allows the tools that don’t immediately require structured data to interact with the loaded data instead of waiting until it’s transformed.
    8. Loading speed is better because the process of transformation happens separately. In addition, the raw data is loaded into the data lake in real time which provides immediate access to information.
    9. Implementation speed is better because the transformation is performed as and when required. ELT can be quickly applied to new sources of data and new information can be uploaded onto the data lake while the queries are being written and the data is analyzed.

Limitations / Shortcomings of ELT

    1. Compliance issue: The legal authorities may not allow the companies to store certain kinds of data. This can be a deterrent as ELT requires accessing and extracting from the raw data. Also, as ELT is integrated with the cloud, some regulations can prohibit their information outside the designated premises or network.
    2. It’s relatively a newer approach and therefore the number of professionals who can provide the service is however on the rise, it’s still inadequate than the market demands.
    3. ELT is efficient only if paired with the storage and processing power of the cloud. That makes it cloud-dependent.
    4. The analysis can take a long time as data is taken through a pre-defined business logic for transformation. This doesn’t allow large volumes of unstructured data to be inspected at a moment’s notice.

Advantages of ETL

    1. ETL’s greatest strength is its structuring of data for more in-depth analysis and examination.
    2. ETL can be a better choice if the business needs quick analysis of data as it is a two-step process. Once the data structure is defined and transformation executed with ETL, the queries are rather efficient. That can produce faster analysis.
    3. It’s easy to comply with legal regulations as any piece of data doesn’t need to leave the source data point.
    4. Compared to ELT ETL is more secure because security checks can be performed or data can be encrypted at the source itself, and only the pieces of information which are necessary can be brought out.
    5. ETL is adaptable to either an on-premise environment or a cloud-based. Data can be taken from on-premise systems and uploaded onto a cloud-based datastore.
    6. ETL can address the data location-related compliance issue that ELT can not. When companies are abiding by data confidentiality and privacy regulations, ETL removes, masks, or encrypts sensitive data before it’s loaded into the data warehouse.
    7. ETL is quite more mature than ELT. It has been used for over two decades. That means the availability of experienced exponents for ETL implementation operations. That can also mean more ETL tools in the marketplace to build data pipelines.

Limitations / Shortcomings of ETL

    1. The loading speed can be slow as it spends time in the staging area to perform transformations on the data so it may not be available quickly enough in the datastore while in ELT data is available instantaneously as it’s extracted.
    2. ETL can be quite a resource-consuming process at the source in terms of the space it occupies on the local hard drives and also the local computing power.
    3. The ELT workflow lacks agility. If the data structure of the new queries or analysis does not match that in the warehouse, the entire transformation process and schema of the data warehouse will need to be modified.
    4. Data volume limitations. ETL is not quite efficient to handle large volumes of data given the time it takes in transformation. It’s best suited for smaller data sets.

The future prospects of ELT

It seems that ELT is becoming the new standard process and there seem quite a few reasons to believe that.

Source system reasons

    • The source data is getting larger from all big data sources and multiple other sources.
    • Unstructured or semi-structured data need complex transformations to make sense.

Target system reasons

    • The target systems are getting more mature in the new era. They are getting more and more robust and scalable for their storage space expansions and computation power.

Design and application choice reason

    • Data model design changes can be kept open to modifications.
    • ETL is the best choice for data science and machine learning.
    • It’s also the best choice for the data from IoT devices, all kinds of sensors, and server logs.

The future prospects of ETL

There is a prevailing skepticism that ETL may soon become obsolete. However, it’s far from the truth. This belief is held by the relatively new data professionals because of their subjective ‘recency bias’ and their deep desire to be considered as the adapters of the new technologies.

Not denying that there do exist some alternative systems such as Hadoop or Spark that can serve the specific data management & integration needs. They can store large amounts of data on quite conventional hardware and are easy to scale up so they can be ideal for large data repositories. They can also quite efficiently store structured and unstructured data parallelly.

However, there are reasons to believe that the ETL approach will still remain relevant in the time to come. Some of them are as follows. Some of the new technology features bring a great advantage to the process. They are…..

    • More Efficient Storage Usage
    • Fast and Accurate Analytics
    • Next-Gen Transformations
    • Privacy, Security, and Compliance
    • In addition to that, the AI-powered interfaces and extensive integration libraries make modern data platforms such as integrate.io rather powerful.

Modern ETL is cloud-based, automated, and can be customized in synch with your business. There are also some trends like…

    • real-time streaming
    • ETLT (an innovative sequence of extract, transformation, load, and then detailed further transformation,) and also a new concept of
    • Data Lakehouse which combines a data lake that supports virtual warehouse structures to hold integrated data supported by AI and other modern ETL processes.

Therefore we see no reason for the ETL approach to get obsolete in the near future and believe that it will continue to thrive as it has been for over four decades.

  •  

How can Data Nectar support you to transform your data into a profiting asset?

This is an era when a large amount of data from various sources has become an inevitable part of the business. Integrating it, managing, organizing, and interpreting it in numerous ways is although an arduous task, and a daunting challenge, it’s vital and inevitable.

Because the quality of such analysis has a direct impact on the quality of your data-driven business decisions. 

Therefore it is more than important that all the data operations are conducted by those who thoroughly understand every business process, and have the prowess to create multi-dimensional visualization of any data.

Data Nectar is the answer to your search for such a partner. We are here to support you in making maximum sense of your data asset through…

    • Data strategy,
    • Business Intelligence, 
    • Data science and analytics, and
    • Application engineering

And keep your data safe, and steer your data-driven decisions.

So feel free to call on +91-79-46034308 or drop a line at [email protected] to discuss your data management and integration needs.

  •  
How data analytics help hospitals deliver better patient care

How data analytics help hospitals deliver better patient care

by | Jun 27, 2022 | Business Intelligence

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Data is everywhere and using it for the business advantage is for everyone and not limited to specific industries. Be it an airline, logistics, eCommerce or hospital. Airlines are apparently are of course more operation intensive, asset heavy and arguably, have to comply with more regulations than hospitals. However best operators are managing it exceptionally well by far most hospitals at keeping costs low and making healthy operational margins without losing the focus on delivering customer experience and value for money. Spicejet airlines, for example, has aptly identified and acted upon key operational parameters that pivots the operational performance: Reducing idle time for planes and keeping the seats filled more often than the competitors. Same way some of busiest airports, Fedex and alike are making a positive impact through their service delivery through most feasible and affordable ways. They all operate in asset heavy service industries.

Above examples are simple and have analogical relevance to how a hospital operates.

There are multiple steps, processes, variables, standards and compliances throughout the customer journey. For example in airlines case, operational process entails steps right from booking to checking in to onboarding and then on flight services, compliances and regulations and a set of checking out process. Every of these processes encompases further smalle pieces of operations spanning across a customer’s experience journey. All these operations involve people and not just machines.

Hospitals these days are facing the same pressure on optimising operational efficiencies and asset utilization that probably airlines, retail and transportation industries have faced for long. As Spicejet, Flipkart, FedEx have stayed competitive in asset- intensive services industries by streamlining operations and getting the max out of their available resources. Hospitals cannot have a long term competitive edge if they keep spending and investing more on infrastructures as short terms fixes to challenges. They must rethink how they are utlisting their available assets in the best possible way to gain ROIs.

To do this, hospitals must look at their data with different lenses like airline, transportation players do. Decision making methodologies must be driven by facts backed with statistics and not only based on a limited set of traditionally available information & experience. It is like having an “operational air traffic control system” for hospital – a centralised repository of vital data and systems around it that has capability to integrate process and analyse a vast variety, velocity and amount of data to learn and predict outcomes. Increased awareness of the potential of data and insights are pushing many healthcare organisations to streamlining operations by using data analytics technologies and tools to mine and process large quantities of data to deliver recommendations to administrative and clinical end users.

Business intelligence and Predictive analytics is playing a key role in improving planning and execution decisions for important care delivery processes and resource utilization( Space, Machines, Human) as well as improving scheduling of staff, availability of key equipment & maintenance. These can lead to better care delivery with optimised asset utilisation and lower costs. Few examples:

Operating Room Utilisation

Operating room is one of most revenue generating assets to the tune of more than 55% of revenues for hospitals. Allocation of OR assets has direct impact on care quality, patient’s experience and preparation staff’s bandwidth. However scheduling them in the most efficient way has been bottlenecked by traditional approaches practiced by most hospitals that involved phones and emails. These means of scheduling and rescheduling is tedious when it comes keeping all stakeholders informed. Obviously the scheduling process is tedious, slow and prone to human errors. Coursey to advance data analytics techniques exploiting cloud, mobile and predictive analytical models that help visualise predicted availability and suggesting time slots for better distribution of hospital resources – Human, Machine & Time – leading to take best out of key assets – the OR.

Surgeons can block the time they need with a single click on a mobile app and the connected apps in the hospitals makes it real time communication and confirmation of OR schedules/availability. Concerned staff can be well aware of any changes/cancellation/additional bookings in real time making the entire planning and execution efficient towards delivering better patient care and higher OR utilisations. At UCHealth in Colorado, scheduling apps allow patients to get treated faster (surgeons release their unneeded blocks 10% sooner than with manual techniques), surgeons gain better control and access (the median number of blocks released by surgeon per month has increased by 47%), and overall utilization (and revenue) increases. With these tools, UCHealth increased per-OR revenue by 4%, which translates into an additional $15 million in revenue annually.

Patient wait times

Same way scheduling for infusions in a function of math and timelines. Mathematically, there are enormous permutations and combinations to pick an optimal slot (to avoid staff and material allocation challenges) for a given type of infusion procedure. Not to mention patient wait time is something that hospitals must minimize.

NewYork-Presbyterian Hospital optimised scheduling based through predictive analytics and machine learning that processed multiple data points around the infusion process to identify patterns and suggest optimised scheduling, resulting in a 45% drop in patient wait times. Infusion center could better manage last-minute add-ons, late cancellations, and no-shows as well as optimize nurses’ work hours.

Emergency Department

Emergency departments are famous for bottlenecks, whether because patients are waiting for lab results or imaging backed up in queues or because the department is understaffed. Analytics-driven software that can determine the most efficient order of ED activities, dramatically reducing patient wait times. When a new patient needs an X-ray and a blood draw, knowing the most efficient sequence can save patients time and make smarter use of ED resources. Software can now reveal historic holdups (maybe there’s a repeated Wednesday EKG staffing crunch that needs fixing) and show providers in real time each patient’s journey through the department and wait times. This allows providers to eliminate recurring bottlenecks and call for staff or immediately reroute patient traffic to improve efficiency. Emory University Hospital, for example, used predictive analytics to forecast patient demand for each category of lab test by time of day and day of week. In so doing, the provider reduced average patient wait times from one hour to 15 minutes, which reduced ED bottlenecks proportionally.

Faster Decisions – ED to inpatient-bed transfer

Predictive tools can also allow providers to forecast the likelihood that a patient will need to be admitted, and provide an immediate estimate of which unit or units can accommodate them. With this information, the hospitalist and ED physician can quickly agree on a likely onboarding flow, which can be made visible to everyone across the onboarding chain. This data-driven approach also helps providers prioritize which beds should be cleaned first, which units should accelerate discharge, and which patients should be moved to a discharge lounge. Using a centralized, data-driven patient logistics system, Sharp HealthCare in San Diego reduced its admit order-to-occupy time by more than three hours.

Efficient Discharge planning

To optimize discharge planning, case managers and social workers need to be able to foresee and prevent discharge delays. Electronic health records or other internal systems often gather data on “avoidable discharge delays” — patients who in the last month, quarter, or year were delayed because of insurance verification problems or lack of transportation, destination, or post-discharge care. This data is a gold mine for providers; with the proper analytics tools, within an hour of a patient arriving and completing their paperwork, a provider can predict with fairly high accuracy who among its hundreds of patients is most likely to run into trouble during discharge. By using such tools, case managers and social workers can create a shortlist of high-priority patients whose discharge planning they can start as soon as the patient is admitted. Using discharge analytics software, MedStar Georgetown University Hospital in Washington, DC, for example, increased its daily discharge volume by 21%, reduced length of stay by half a day, and increased morning discharges to 24% of all daily discharges.

Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, analytics tools can lower health care costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place.

How data analytics help hospitals deliver better patient care

How data analytics help hospitals deliver better patient care

by | Jun 27, 2022 | Business Intelligence

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Data is everywhere and using it for the business advantage is for everyone and not limited to specific industries. Be it an airline, logistics, eCommerce or hospital. Airlines are apparently are of course more operation intensive, asset heavy and arguably, have to comply with more regulations than hospitals. However best operators are managing it exceptionally well by far most hospitals at keeping costs low and making healthy operational margins without losing the focus on delivering customer experience and value for money. Spicejet airlines, for example, has aptly identified and acted upon key operational parameters that pivots the operational performance: Reducing idle time for planes and keeping the seats filled more often than the competitors. Same way some of busiest airports, Fedex and alike are making a positive impact through their service delivery through most feasible and affordable ways. They all operate in asset heavy service industries.

Above examples are simple and have analogical relevance to how a hospital operates.

There are multiple steps, processes, variables, standards and compliances throughout the customer journey. For example in airlines case, operational process entails steps right from booking to checking in to onboarding and then on flight services, compliances and regulations and a set of checking out process. Every of these processes encompases further smalle pieces of operations spanning across a customer’s experience journey. All these operations involve people and not just machines.

Hospitals these days are facing the same pressure on optimising operational efficiencies and asset utilization that probably airlines, retail and transportation industries have faced for long. As Spicejet, Flipkart, FedEx have stayed competitive in asset- intensive services industries by streamlining operations and getting the max out of their available resources. Hospitals cannot have a long term competitive edge if they keep spending and investing more on infrastructures as short terms fixes to challenges. They must rethink how they are utlisting their available assets in the best possible way to gain ROIs.

To do this, hospitals must look at their data with different lenses like airline, transportation players do. Decision making methodologies must be driven by facts backed with statistics and not only based on a limited set of traditionally available information & experience. It is like having an “operational air traffic control system” for hospital – a centralised repository of vital data and systems around it that has capability to integrate process and analyse a vast variety, velocity and amount of data to learn and predict outcomes. Increased awareness of the potential of data and insights are pushing many healthcare organisations to streamlining operations by using data analytics technologies and tools to mine and process large quantities of data to deliver recommendations to administrative and clinical end users.

Business intelligence and Predictive analytics is playing a key role in improving planning and execution decisions for important care delivery processes and resource utilization( Space, Machines, Human) as well as improving scheduling of staff, availability of key equipment & maintenance. These can lead to better care delivery with optimised asset utilisation and lower costs. Few examples:

Operating Room Utilisation

Operating room is one of most revenue generating assets to the tune of more than 55% of revenues for hospitals. Allocation of OR assets has direct impact on care quality, patient’s experience and preparation staff’s bandwidth. However scheduling them in the most efficient way has been bottlenecked by traditional approaches practiced by most hospitals that involved phones and emails. These means of scheduling and rescheduling is tedious when it comes keeping all stakeholders informed. Obviously the scheduling process is tedious, slow and prone to human errors. Coursey to advance data analytics techniques exploiting cloud, mobile and predictive analytical models that help visualise predicted availability and suggesting time slots for better distribution of hospital resources – Human, Machine & Time – leading to take best out of key assets – the OR.

Surgeons can block the time they need with a single click on a mobile app and the connected apps in the hospitals makes it real time communication and confirmation of OR schedules/availability. Concerned staff can be well aware of any changes/cancellation/additional bookings in real time making the entire planning and execution efficient towards delivering better patient care and higher OR utilisations. At UCHealth in Colorado, scheduling apps allow patients to get treated faster (surgeons release their unneeded blocks 10% sooner than with manual techniques), surgeons gain better control and access (the median number of blocks released by surgeon per month has increased by 47%), and overall utilization (and revenue) increases. With these tools, UCHealth increased per-OR revenue by 4%, which translates into an additional $15 million in revenue annually.

Patient wait times

Same way scheduling for infusions in a function of math and timelines. Mathematically, there are enormous permutations and combinations to pick an optimal slot (to avoid staff and material allocation challenges) for a given type of infusion procedure. Not to mention patient wait time is something that hospitals must minimize.

NewYork-Presbyterian Hospital optimised scheduling based through predictive analytics and machine learning that processed multiple data points around the infusion process to identify patterns and suggest optimised scheduling, resulting in a 45% drop in patient wait times. Infusion center could better manage last-minute add-ons, late cancellations, and no-shows as well as optimize nurses’ work hours.

Emergency Department

Emergency departments are famous for bottlenecks, whether because patients are waiting for lab results or imaging backed up in queues or because the department is understaffed. Analytics-driven software that can determine the most efficient order of ED activities, dramatically reducing patient wait times. When a new patient needs an X-ray and a blood draw, knowing the most efficient sequence can save patients time and make smarter use of ED resources. Software can now reveal historic holdups (maybe there’s a repeated Wednesday EKG staffing crunch that needs fixing) and show providers in real time each patient’s journey through the department and wait times. This allows providers to eliminate recurring bottlenecks and call for staff or immediately reroute patient traffic to improve efficiency. Emory University Hospital, for example, used predictive analytics to forecast patient demand for each category of lab test by time of day and day of week. In so doing, the provider reduced average patient wait times from one hour to 15 minutes, which reduced ED bottlenecks proportionally.

Faster Decisions – ED to inpatient-bed transfer

Predictive tools can also allow providers to forecast the likelihood that a patient will need to be admitted, and provide an immediate estimate of which unit or units can accommodate them. With this information, the hospitalist and ED physician can quickly agree on a likely onboarding flow, which can be made visible to everyone across the onboarding chain. This data-driven approach also helps providers prioritize which beds should be cleaned first, which units should accelerate discharge, and which patients should be moved to a discharge lounge. Using a centralized, data-driven patient logistics system, Sharp HealthCare in San Diego reduced its admit order-to-occupy time by more than three hours.

Efficient Discharge planning

To optimize discharge planning, case managers and social workers need to be able to foresee and prevent discharge delays. Electronic health records or other internal systems often gather data on “avoidable discharge delays” — patients who in the last month, quarter, or year were delayed because of insurance verification problems or lack of transportation, destination, or post-discharge care. This data is a gold mine for providers; with the proper analytics tools, within an hour of a patient arriving and completing their paperwork, a provider can predict with fairly high accuracy who among its hundreds of patients is most likely to run into trouble during discharge. By using such tools, case managers and social workers can create a shortlist of high-priority patients whose discharge planning they can start as soon as the patient is admitted. Using discharge analytics software, MedStar Georgetown University Hospital in Washington, DC, for example, increased its daily discharge volume by 21%, reduced length of stay by half a day, and increased morning discharges to 24% of all daily discharges.

Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, analytics tools can lower health care costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place.